Stuart Russell, Curriculum Vitae

Email russell@cs.berkeley.edu
Home page http://www.cs.berkeley.edu/~russell

Education

B.A. (Hons.) 1st Class, Physics, Wadham College, University of Oxford, 1979-82.
Ph.D., Computer Science, Stanford University, 1982-86.

Employment history

Honours, Awards

• 1st Scholar, St. Paul's School, London, 1974-78.
• Distinction, UK National Mathematical Competition, 1977.
• Wadham College Major Scholar 1979-82.
• NATO Scholar, 1982-85.
• NSF Presidential Young Investigator, 1990-95.
• UK SERC Visiting Professorship, 1992.
• Computers and Thought Award, 1995 (awarded biennially by the International Joint Committee on Artificial Intelligence for the most significant contributions to artificial intelligence).
• Class of 1960s Visiting Scholar, Williams College, 1995.
• Miller Professorship, UC Berkeley, 1996.
• Fellow of the American Association for Artificial Intelligence, 1997.
• Forsythe Lecturer, Stanford University, 1998.
• Selected as one of 15 ``Bay Area Visionaries for the New Millenium,'' SF Magazine, Jan 2000.
• Jon Postel Distinguished Lecturer, UCLA, 2000.
• Chancellor's Professorship, UC Berkeley, 2000.
• Michael H. Smith and Lotfi A. Zadeh Chair in Engineering, UC Berkeley, 2001.
• Fellow of the Association for Computing Machinery, 2003.
• Triangle Distinguished Lecturer (Duke University, University of North Carolina, North Carolina State University), 2004.
• Distinguished Lecturer, University of Illinois at Urbana-Champaign, 2004.
• Distinguished Lecturer, University of Illinois at Chicago, 2004.
• Distinguished Lecturer, University of Michigan, 2005.
• Distinguished Lecturer, University of Toronto, 2005.
• ACM Karl V. Karlstrom Outstanding Educator Award, 2005.
• Distinguished Lecturer, University of California, Irvine, 2006.
• Distinguished Lecturer, University of Southern California, 2011.
• Fellow, American Association for the Advancement of Science, 2011.
• Chaire Blaise Pascal, Fondation de l'École Normale Supérieure, Paris, 2012.
• Chaire d'Excellence, Agence Nationale de la Recherche, France, 2012.
• Distinguished Lecture, Technical University of Berlin, 2014.
• Mitchell Prize, 2014, jointly awarded by the American Statistical Association and the International Society for Bayesian Analysis to recognize "an outstanding paper that describes how a Bayesian analysis has solved an important applied problem," for the paper "NET-VISA: Network Processing Vertically Integrated Seismic Analysis".
• World Technology Award (Policy), 2015.
• Inaugural AAAI/EAAI Outstanding Educator Award, 2016.
• Amity Booker Prize for "Artificial Intelligence: A Modern Approach", 2017.
• TED lecturer, 2017.
• Honorary degree in Global Governance, University of Rome Tor Vergata, 2017.
• Strachey Lecturer, Oxford University, 2018.
• Honorary Fellow, Wadham College, Oxford, 2018.
• Awards for Slaughterbots
  • Golden Dolphin Award (viral category), 9th Cannes Corporate Media & TV Awards, 2018.
  • Gold Award (viral category), Brand Film Festival, London, 2018.
  • Video of the Year, Prolific North Awards, Manchester, 2018.
  • Award of Excellence, IndieFEST Film Awards, 2018.
• Distinguished Fellow, Stanford University Human-Centered AI Initiative, 2018.
• Associate Fellow, Royal Institute for International Affairs (Chatham House), 2019.
• AAAI Feigenbaum Prize, 2019.
• Andrew Carnegie Fellowship, 2019.
• Amity Researcher Award in Artificial Intelligence, 2019.
• Member, International Academy of Digital Arts and Sciences, 2019.
• Awards for Human Compatible:
  • Best science books of 2019, Daily Telegraph.
  • Best technology books of 2019, Financial Times.
  • Best science, nature, and ideas books of 2019, The Guardian.
  • Top Ten Technology Books Of 2019, Forbes Magazine.
  • The Best Books of 2019 for Ethical Leaders, Notre Dame Deloitte Center for Ethical Leadership.
  • Thirty books to help us understand the world in 2020, The Guardian.
• Turing Lecturer, Alan Turing Institute, London, 2020.
• Officer of the Most Excellent Order of the British Empire "for services to artificial intelligence research," 2021.
• Reith Lecturer, 2021.
• AI2050 Fellowship, Schmidt Futures Foundation, 2022.
• IJCAI Research Excellence Award, 2022.
• ACM/AAAI Allen Newell Award, 2022.
• Policy Leader Award (inaugural), Center for AI and Digital Policy, Washington, DC, 2023.
• Time Magazine 100 Most Influential People in AI (inaugural list), 2023.

Paper Prizes

• Best Paper Nomination, European Conference on AI, 1992, for ``Efficient Memory-Bounded Search.''
• Best Industry-Related Paper Award (Honorable Mention), International Conference on Pattern Recognition, 1994, for ``Towards Robust Automatic Traffic Scene Analysis in Real-Time.''
• Distinguished Paper Prize, International Joint Conference on Artificial Intelligence, 1997, for ``Object Identification in a Bayesian Context.''
• Best Paper Prize (Honorable Mention), International Conference on Machine Learning, 1999, for ``Policy Invariance under Reward Transformations.''
• Paper selected for journal special issue, International Conference on Speech and Language Processing, 1999, for ``Probabilistic modeling with Bayesian networks for automatic speech recognition.''
• Paper selected for journal special issue, International Joint Conference on Artificial Intelligence, 2001, for ``Approximate inference for first-order probabilistic languages.''
• Paper selected for journal special issue, International Conference on Automated Planning and Scheduling, 2008, for ``Angelic Hierarchical Planning: Optimal and Online Algorithms.''
• 10-Year paper Prize (Honorable Mention), ICML 2009 (papers in ICML 1999), for ``Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping.''
• Outstanding Student Paper Award Winner (to Nimar Arora), American Geophysical Union Fall Meeting, 2011 , for ``Scalable Probabilistic Inference for Global Seismic Monitoring.''
• Highlight Paper, Conference on Artificial Intelligence and Statistics, Reykjavik, 2013, for ``NET-VISA: Network Processing Vertically Integrated Seismic Analysis.''
• Best Paper Prize, NeurIPS-20 Workshop on Cooperative AI, Montreal, 2020, for ``Benefits of Assistance over Reward Learning.''
• Best Paper Prize, ICML-23 Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023, for ``Bridging RL Theory and Practice with the Effective Horizon.''

Selected Invited/Keynote Speeches

• Invited Speaker, European Conference on Economics and Game Theory, 1992.
• Invited Speaker, 3rd Annual NEC Symposium on Computer Science, Princeton, 1992.
• Keynote Speaker, IEEE Workshop on Imprecise and Approximate Computation, Phoenix, 1992.
• Invited Speaker, Annual Meeting of the American Philosophical Association, April 1993.
• Keynote Speaker, International Conference on Economics and Game Theory, Marseilles, 1994.
• Keynote Speaker, IJCAI Workshop on Anytime Algorithms, Montreal, 1995.
• Invited Speaker, Reconnaisance des Formes et Intelligence Artificielle, Rennes, France, 1996.
• Invited Speaker, Kuenstliche Intelligenz (KI96 Conference), Dresden, Germany, 1996.
• Invited Speaker, European Conference on Machine Learning, Prague, 1997.
• Invited Speaker, Neural Information Processing Systems, Denver, 1997.
• Invited Speaker, Vancouver Cognitive Science Conference, 1998
• Keynote Speaker, Artificial Intelligence (AI-98), Vancouver, 1998.
• Invited Speaker, Theoretical Aspects of Rationality and Knowledge (TARK 98), Chicago, July 1998
• Invited Speaker, ACM Conference on Computational Learning Theory, Conference on Uncertainty in Artificial Intelligence, and International Conference on Machine Learning, Madison, Wisconsin, 1998.
• Keynote Speaker, Discovery Science, Kyoto, Japan, 1999.
• Keynote Speaker, Workshop on Automated Speech Recognition and Understanding, Keystone, Colorado, 1999.
• Invited Speaker, AAAI Workshop on Uncertainty in Computation, 2000.
• Invited Speaker, International Fuzzy Systems Association Conference, Vancouver, 2001.
• Keynote Speaker, IJCAI Workshop on Reasoning under Uncertainty in Robotics, Seattle, 2001.
• Invited Speaker, 13th Dutch-Belgian AI Conference, Amsterdam, 2001.
• Invited Speaker, 9th IFSA World Congress, 2001
• Keynote Speaker, Mexican International Conference on AI, 2002
• Keynote Speaker, Dutch STW (national science foundation) meeting on learning systems, Nijmegen, Holland, 2002
• Keynote Speaker, KTH (Royal Institute of Technology) 175th Anniversary, Stockholm, 2002
• Invited Participant, National Academy of Sciences Japanese-American Frontiers of Science Symposium, 2003.
• Keynote Speaker, KDD Workshop on Record Linkage, 2003.
• Invited Speaker, Wayland Collegium, Brown University, 2005.
• Invited Tutorial Speaker, Neural Information Processing Systems (NeurIPS), 2005.
• Invited Speaker, The Dartmouth Artificial Intelligence Conference: The next 50 years, 2006.
• Keynote Speaker, International Conference on Inductive Logic Programming, Santiago de Compostela, Spain, 2006.
• Keynote Speaker, International Conference on Machine Learning and Cybernetics, Dalian, China, 2006.
• Invited Speaker, Graduate Summer School on Probabilistic Models of Cognition, Institute for Pure and Applied Mathematics, UCLA, 2007.
• Invited Tutorial Speaker, Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI-07), 2007.
• Keynote Speaker, Association Francaise d'Intelligence Artificielle (AFIA-07), Grenoble, France, 2007.
• Keynote Speaker, Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08), Chicago, 2008.
• Keynote Speaker, NeurIPS Workshop on Probabilistic Programming Languages, Vancouver, 2008.
• Keynote Speaker, ISS-09 Conference, UN Comprehensive Test Ban Treaty Organization, Vienna, 2009.
• Keynote Speaker, COLT/ICML/UAI Workshop on Abstraction in Reinforcement Learning, 2009.
• Keynote Speaker, NeurIPS Workshop on Bounded Rationality and Cognition, Vancouver, 2009.
• Invited Speaker, Plenary Session of the UN Comprehensive Test Ban Treaty Organization, Vienna, 2010.
• Keynote Speaker, Third International Pao-Lu Hsu Conference, Hangzhou, China, 2011.
• Keynote Speaker, AAAI Workshop on Generalized Planning, San Francisco, 2011.
• Keynote Speaker, Ninth Symposium on Abstraction, Reformulation, and Approximation, Cardona, Spain, 2011.
• Keynote Speaker, 10th International Conference on Complexity in Acute Illness, Bonn, 2011.
• Invited Speaker, Plenary Session of the UN Comprehensive Test Ban Treaty Organization, Vienna, 2011.
• Invited Speaker, Plenary Session of the UN Comprehensive Test Ban Treaty Organization Waveform Expert Group, Vienna, 2012.
• Keynote Speaker, ECAI Workshop on Intelligent Agents in Urban Simulations and Smart Cities, Montpellier, France, 2012.
• Keynote Speaker, NeurIPS Workshop on Probabilistic Programming, Lake Tahoe, California, 2012.
• Invited Speaker, American Geophysical Union Fall Meeting, San Francisco, 2012.
• Invited Speaker "Grand Lecture", Laboratoire d'Informatique, Grenoble, 2013.
• Invited Speaker, Special Colloquium, KU Leuven, 2013.
• Invited Speaker "Grand Seminaire", Univ. Nantes, 2013.
• Invited Speaker, Colloquium Sorbonne-Universités, Paris, 2013.
• Keynote Speaker, ICAPS Workshop on Planning and Robotics, Rome, 2013.
• Invited Speaker, Colloquium Patavina, Padua, 2013.
• Keynote Speaker, Conference on the Philosophy and Theory of AI, Oxford, 2013.
• Keynote Speaker, Conference on Information Processing and Management of Uncertainty, Montpellier, 2014.
• Invited Speaker, Distinguished Lecture, Technical University of Berlin, 2014.
• Keynote Speaker, Conference on the Future of Artificial Intelligence, Puerto Rico, 2015.
• Invited Speaker, World Economic Forum, Davos, 2015.
• Invited Speaker, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), Austin, 2015.
• Invited Speaker, American Physical Society March Meeting, San Antonio, 2015.
• Invited Speaker, United Nations Convention on Certain Conventional Weapons, Geneva, 2015.
• Invited Speaker, Centre for Research in the Arts, Humanities and Social Sciences, Cambridge, 2015.
• Keynote Speaker, Joint session of International Conference on Automated Planning and Scheduling and Symposium on Combinatorial Search, Jerusalem, 2015.
• Invited Speaker, Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-15), Buenos Aires, 2015.
• Invited Speaker, Nobel Week Dialogue, Stockholm, 2015.
• Invited Speaker, World Economic Forum, Davos, 2016.
• Keynote Speaker, Ethics Forum, California State University Monterey Bay, 2016.
• Invited Speaker, CERN Colloquium, Geneva, 2016.
• Invited Speaker, Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Pheonix, 2015.
• Invited Speaker, IARPA Colloquium, Washington, DC, 2016.
• Keynote Speaker, Herbst Symposium, University of Colorado at Boulder, 2016.
• Invited Speaker, Milken Global Institute, Los Angeles, 2016.
• Keynote Speaker, Seoul Digital Forum, South Korea, 2016.
• Keynote Speaker, Strata Big Data Conference, London, 2016.
• Keynote Speaker, Bund Financial Summit, Shanghai, 2016.
• Keynote Speaker, Simons Institute Workshop on Uncertainty on Computation, Berkeley, 2016.
• Keynote Speaker, Bay Area Robotics Symposium, Stanford, 2016.
• Invited Speaker, NYU Symposium on Ethics of AI, New York, 2016
• Invited Speaker, UN Office of the High Representative for Disarmament Affairs, New York, 2016.
• Keynote Speaker, Nikkei/Financial Times AI Summit, Palo Alto, 2016
• Invited Speaker, National Academy of Science Symposium on National Security, Washington, DC,, 2016.
• Keynote Speaker, Conference on Beneficial AI, Asilomar, 2017.
• Invited Speaker, World Economic Forum, Davos, 2017.
• Invited Speaker, UN Secretary-General's Advisory Board on Disarmament Matters, Geneva, 2017.
• Invited Speaker, Nobel Prize Dialogue, Tokyo, 2017.
• Keynote Speaker, Global Machine Intelligence Summit, Beijing, 2017.
• Invited Speaker, TED Global Conference, Vancouver, 2017.
• Keynote Speaker, Conference on Governance of Emerging Technologies, Phoenix, 2017.
• Keynote Speaker, World Strategy Forum, Seoul, 2017.
• Keynote Speaker, AI Vision Conference, San Francisco, 2017.
• Invited Speaker, National Academies Colloquium on Selected Topics in Artificial Intelligence, Washington, DC, 2017.
• Keynote Speaker, Big Data Summit, Melbourne, 2017.
• Keynote Speaker, International Joint Conference on Artificial Intelligence, Melbourne, 2017.
• Keynote Speaker, World IP Expo, London, 2017.
• Keynote Speaker, World Summit on AI, Amsterdam, 2017.
• Keynote Speaker, Samsung AI Forum, Seoul, 2017.
• Keynote Speaker, OECD Conference on Artificial Intelligence, Paris, 2017.
• Invited Speaker, UN Group of Governmental Experts on Lethal Autonomous Weapons Systems, Geneva, 2017.
• Keynote Speaker, OECD Global Strategy Group Annual Meeting, Paris, 2017.
• Keynote Speaker, Psychology of Technology conference, Berkeley, 2017.
• Plenary Speaker, Entretiens de Royaumont, France, 2017.
• Keynote Speaker, 25th anniversary, Budapest University of Technology and Economics, Budapest, 2018.
• Invited Speaker, World Government Summit, Dubai, 2018.
• Invited Speaker, Launch of French National AI Strategy, Paris, 2018.
• Keynote Speaker, China Shanghai International Trade Forum, Shanghai, 2018.
• Plenary Speaker, United Nations AI for Good Global Summit, Geneva, 2018.
• Keynote Speaker, Swiss Economic Forum, Interlaken, 2018.
• Invited Speaker, Human Rights Watch Global Summit, Zurich, 2018.
• Keynote Speaker, China International Artificial Intelligence Summit, Dalian, China, 2018.
• Strachey Lecturer, Oxford University, 2018.
• Turing Lecturer, Federated Logic Conferences Summit on Machine Learning and Formal Methods, Oxford, 2018.
• Keynote Speaker, Conference on Uncertainty in Artificial Intelligence, Monterey, California, 2018.
• Keynote Speaker, World AI Conference, Shanghai, 2018.
• Invited Speaker, World Economic Forum, Tianjin, China, 2018.
• Invited Speaker, "Leaders Who Inspire" lecture series, Northeastern University, Boston, 2018.
• Keynote Speaker, First International Conference on Probabilistic Programming, MIT, 2018.
• Invited Speaker, United Nations Science-Policy-Business Forum on the Environment, Paris, 2018.
• Keynote Speaker, Amazon Research Day, Seattle, 2018.
• Invited Speaker, Westminster Abbey, London, 2018.
• Keynote Speaker, Third Conference on Beneficial AI, Puerto Rico, 2019.
• Invited Speaker, United Nations Secretary-General's Senior Management Group, New York, 2019.
• Invited Speaker, Tweede Kamer (Dutch Parliament), The Hague, 2019.
• Keynote Speaker, Augmented Intelligence Summit, Santa Cruz, California, 2019.
• Keynote Speaker, Joint National Academies/Royal Society meeting on AI, Washington, DC, 2019.
• Museum Lecture, Bohemian Grove, CA, 2019.
• Keynote Speaker, CogX Conference, London, 2019.
• Plenary Speaker, Chatham House Annual Conference, London, 2019.
• Keynote Speaker, Machine Intelligence 19, Windsor, England, 2019.
• Invited Speaker, National Academies Meeting on Research Governance for Climate Intervention, Stanford, 2019.
• Keynote Speaker, Digital Technology Exposition, London, 2019.
• Keynote Speaker, World Summit on AI, Amsterdam, 2019.
• Invited Speaker, Global Forum on AI for Humanity, Paris, 2019.
• Invited Speaker, Colloquium Sorbonne-Universités, Paris, 2019.
• Keynote Speaker, Bay Area Robotics Symposium, Berkeley, 2019.
• Keynote Speaker, Nanjing Forum, Nanjing University, China, 2019.
• Distinguished Lecture, Peking University, China, 2019.
• Distinguished Lecture, Tsinghua University, China, 2019.
• Invited Speaker, Nobel Week Dialogues, Stockholm, 2019.
• Keynote Speaker, AAAI Conference, New York, 2020.
• Inaugural Annual Lecture, Institute for Practical Ethics, UC San Diego, 2020.
• Keynote Speaker, Bank of England Conference on Machine Learning and AI, London, 2020.
• Turing Lecture, Alan Turing Institute, London, 2020.
• Invited Speaker, World Peace Forum, Beijing, 2020.
• Keynote Speaker, Seoul Economic Forum, Seoul, 2020.
• Keynote Speaker, European Conference on AI, Santiago de Compostela, 2020.
• Keynote Speaker, United Nations Global Summit on AI for Good, Geneva, 2020.
• Inaugural Forum Humanum Lecture, NYU/New School, 2020.
• Invited Speaker, Paris Peace Forum, 2020
• Invited Speaker, CERN Sparks Launch Event, 2020
• Keynote Speaker, Fujitsu Labs Annual Technical Symposium, Mountain View, 2020
• Keynote Speaker, Conference on Governance Of and By Digital Technology, International Risk Governance Center, Lausanne, 2020
• Keynote Speaker, The Hague Conference on Responsible AI, Ministry of Peace and Justice and Ministry of Foreign Affairs of the Netherlands, 2020
• Keynote Speaker, NeurIPS Workshop on Broader Impacts of AI, 2020
• Keynote Speaker, Mars AI Festival, 2020
• Keynote Speaker, AXA AI and Security Week, 2020
• Keynote Speaker, 3rd World Economic Forum Workshop on AI Economic Futures, San Francisco, 2020
• Invited Debater, Munk Debate on "The Rise of Thinking Machines", Toronto, 2021
• Invited Speaker, Applied Physics Lab Colloquium, Johns Hopkins University, 2021
• Keynote Speaker, MIT China Innovation and Entrepreneurship Forum, 2021
• Invited Speaker, Microsoft Research New England Colloquium, 2021
• 18th Kim Okgill Memorial Lecture, Ewha Women's University, Seoul, 2021
• Keynote Speaker, Universidade Federal do Rio Janeiro 100th anniversary celebration, 2021
• Invited Speaker, UNESCO High-Level Dialogue: Ensuring Inclusion in the AI world, Paris, 2021
• Invited Speaker, CERN Sparks Dialogue, 2021
• Invited Lecture, CogX AI Festival, London, 2021
• Keynote Lecture, European Union Humane AI Network, 2021
• Keynote Lecture, CTBTO Science and Technology Summit, Vienna, 2021
• Keynote Lecture, Plate-Forme Intelligence Artificielle, Bordeaux, France, 2021
• Invited Lecture, Ecole des Hautes Etudes Commerciales de Paris (HEC), Paris, 2021
• Invited Panelist, National Academies Workshop on Artificial Intelligence. Washington DC., 2021
• Keynote Lecture, National Academies Conference on AI, ML, and the Human Condition, Los Alamos, 2021
• Inaugural Prato Dialogue Distinguished Speaker, Prato Dialogue Series, Monash U., 2021
• Keynote Lecture, IJCAI-21 Workshop on Adverse Impacts of Artificial Intelligence, Montreal, 2021
• Invited Lecture, 50th anniversary Dalle Molle Foundation & 30th anniversary IDIAP research institute, Martigny, Switzerland, 2021
• Invited Lecture, CERN Sparks Forum, Geneva, 2021
• Keynote Lecture, International Conference on Logic Programming, 2021
• Keynote Lecture, KI21 (German national conference on AI), 2021
• Keynote Lecture, Responsible Machine Learning Summit, UC Santa Barbara, 2021
• Keynote Lecture, Brazilian Academy of Science, 105th Anniversary Magna Conference, 2021
• Reith Lectures, BBC, London, Manchester, Edinburgh, Newcastle, 2021
• Keynote Lecture, Open Data Science Conference, San Francisco, 2021
• Keynote Lecture, Munhwa Global AI Summit, Seoul, South Korea, 2021
• Keynote Lecture, Council of Europe 6th Plenary Conference on AI, Strasbourg, 2021
• Keynote Lecture, AI International Symposium, Korea Advanced Institute of Science and Technology (KAIST), Seoul, South Korea, 2021
• Keynote Lecture, Conference for the 50th Anniversary of the Founding of Bangladesh, Dhaka, Bangladesh, 2021
• Keynote Lecture, Epoch Foundation 30th Anniversary, Taipei, Taiwan, 2021
• Plenary Invited Lecture, Workshop on Interpretability, Safety and Security in AI, Alan Turing Institute, London, 2021
• Invited Lecture, OECD Education Policy Meeting on High Performing Systems for Tomorrow, OECD, Paris, 2022
• Plenary Lecture, CTO Forum on Future Directions in AI, San Francisco, 2022
• Distinguished Lecture, Department of Philosophy and College of Engineering, CalPoly San Luis Obispo, 2022
• Keynote Lecture, Intelligent User Interfaces (IUI) conference, Helsinki, 2022
• Inaugural Distinguished Lecture, Schmidt AI and Emerging Tech Symposium, Yale University, 2022
• Inaugural Distinguished Fellows Lecture, Munk School of Global Affairs & Public Policy, University of Toronto, 2022
• Institute Lecture, Institute for Science and Technology Austria, Vienna, 2022
• Invited Speaker, World Economic Forum, Davos, 2022
• Invited Speaker, Nobel Dialogues, Singapore, 2022
• Plenary Award Lecture (Research Excellence), International Joint Conference on Artificial Intelligence, Vienna, 2022
• Bernard Price Memorial Lecture, South African Institute of Electrical Engineers, Johannesberg, 2022
• Invited Speaker, Nobel Week Dialogues, Stockholm, 2022
• Lord Speaker's Lecture, House of Lords, London, 2022
• Invited Speaker, World Economic Forum, Davos, 2023
• Keynote Lecture, OECD Working Party on Artificial Intelligence Governance, Paris, 2023
• Distinguished Lecture, Computer Science Department, Harvard University, 2023
• Distinguished Lecture, Institute for Intelligent Information Systems, Tsinghua University, Beijing, 2023
• Invited Lecture, UNESCO Global Forum on the Ethics of AI, Prague, 2023
• Keynote lecture, World AI Festival Cannes, France, 2023
• Keynote lecture, Summit on Responsible Use of AI in the Military Domain, The Hague, Netherlands, 2023
• Keynote Lecture, Latin American and Caribbean Conference on Autonomous Weapons, San Jose, Costa Rica, 2023
• Keynote lecture, Existential Risk Observatory, Amsterdam, 2023
• Invited lecture, National Aerospace University, Kharkiv, Ukraine, 2023
• Keynote lecture, OECD AI Expert Group, Paris, 2023
• Invited testimony, House of Lords Select Committee on Autonomous Weapons, London, 2023
• Keynote Lecture, Brookings Institute, Washington, DC, 2023
• Keynote lecture, Comprehensive Nuclear-Test-Ban Treaty Organization, Vienna, 2023
• Invited lecture, Meeting of Group of Government Experts on Lethal Autonomous Weapons Systems, United Nations, Geneva, 2023
• Keynote lecture, IEEE International Conference on Circuits and Systems, Monterey, California, 2023
• Invited Testimony on AI, United Kingdom Competition and Markets Authority, 2023
• Keynote lecture, UNESCO Ministerial Meeting on Generative AI in Education, 2023
• Keynote lecture, Annual Conference of the Beijing Association for AI, Beijing, 2023
• Invited Testimony, UK House of Lords Select Committee on Autonomous Weapons, London, 2023
• Keynote Lecture, United Nations Conference on AI for Good, Geneva, 2023
• Invited Testimony, Judiciary Committee of the United States Senate, Washington, DC, 2023
• Invited Testimony, United States National AI Advisory Committee, Washington, DC, 2023
• Invited Testimony, UK House of Lords Committee on Communications and Digital Policy, London, 2023
• Keynote Lecture, UNESCO Digital Learning Week, Paris, 2023
• Keynote Lecture, Annual Meeting of the Executive Committee of the Global Partnership on AI, 2023

Professional and Service

Government and international organizations

Scientific societies

Member, ACM Turing Award Committee, 2023-present
Member, ACM Grace Murray Hopper Award Committee, 2013-2016
Chair, ACM Grace Murray Hopper Award Committee, 2014-2015
Member, ACM Awards Committee, 2014-2016
Chair, ACM Task Force on Lethal Autonomous Weapons, 2020-present
Member, AAAI Conference Awards Committee, 2014
Member, Nominating Panel, Japan Prize
Member, Nominating Panel, Kyoto Prize
Member, Nominating Panel, BBVA Foundation Prizes
Referee, Templeton Prize

Chairman, British Scientists Abroad, 1993-96
Organizing Committee, Newsletter Editor, and Spokesperson, British Scientists Abroad, 1990-92

Academic advisory boards

Editorial and reviewing roles

Co-Chair, Hastings Center Workshops on Control and Responsible Innovation in the Development of Autonomous Machines (2017-19)
Co-Chair, Conference on the Future of AI, 2015
Chair, AAMAS Workshop on the Future of Artificial Intelligence, 2014
Chair, IJCAI Panel on the Future of Artificial Intelligence, 2013
Chair, DARPA Workshop on Human-Level AI, 2004
Co-Chair, AAAI Fall Symposium on Learning Complex Behaviours, 1996
Co-Chair, Workshop on Learning in Bayesian Networks, NeurIPS 95
Program and Conference Co-Chair, Int'l Conference on Machine Learning, 1995
Program and Symposium Chair, AAAI Symposium on AI and Limited Rationality, Stanford, 1989

Program Area Chair, AI and Cognitive Science areas, NeurIPS 96
Program Area Chair (Decision Theory, Machine Learning, Search), AAAI 94
Program Area Chair (Machine Learning), AAAI 90

Scientific Programme Committee, UN Comprehensive Nuclear-Test-Ban Treaty Organization Science & Technology Conference, 2020-21
Steering Committee, Global Forum on AI for Humanity, Paris, 2019
Organizing and Program Commiteee, Conference on AI Ethics and Social Impact, 2019; AAAI 19 Workshop on Safe AI; First International Workshop on Artificial Intelligence Safety Engineering, 2018; United Nations AI for Good Global Summit, 2018, 2019, 2023; CogSci 2017 Workshop on Cooperative Social Intelligence, 2017; 3rd International Workshop on Data Science in High Energy Physics, 2017; Beneficial AI Conference, 2017; IJCAI Workshop on Ethics of AI, 2016; AAAI Workshop on Ethics and Social Impact of AI, 2016; International Joint Conference on Artificial Intelligence, 2016; POPL Workshop on Probabilistic Programming Systems, 2016; Workshop on Data Science at the Large Hadron Collider, 2015; NeurIPS Workshop on Bounded Rationality and Rational Metareasoning, 2015; AAAI Workshop on Ethics of AI, 2015; NeurIPS Workshop on Probabilistic Programming Languages, 2014; ICML Workshop on Structured Learning on Graphs, 2013; International Workshop on Statistical Relational AI, 2012; NeurIPS Workshop on Probabilistic Programming Languages, 2011; AAAI Spring Symposium on Decision-Theoretic Planning, 1994; AAAI Fall Symposium on Planning and Learning in Games, 1993; IEEE Workshop on Imprecise and Approximate Computation, 1992; Workshop on Theoretical and Practical Design of Rational Agents, IJCAI 91.

Member, Program Committee, Conference of the Association the Advancement of AI (AAAI) 1988, 1990, 1992; International Conference on Machine Learning 1992, 1993, 1994, 1996, Third International Conference on Multistrategy Learning, 1993; International Conference on Knowledge Representation and Reasoning, 1994, 1998, 2002; ACM Workshop on Computational Learning Theory, 1998; European Conference on AI, 1996; European Conference on Machine Learning, 2000; KDD Workshop on Record Linkage, 2003; ICML Workshop on Statistical Relational Learning, 2004; ICML Workshop on Relational Reinforcement Learning, 2004; ICMl Workshop on Machine Learning for Clinical Data Analysis, 2012; Second Conference on Meaningful Use of Complex Medical Data, 2012.

National Science Foundation Review Panelist
Fellowship Panel, National Research Council
Proposal Reviewer, National Science Foundation, California MICRO Program, Air Force Office of Scientific Research, France-Berkeley Foundation
Panelist, Defense Threat Reduction Agency, 2001

Commentator, Behavioral and Brain Sciences
Reviewer, Nature, Science, Science Robotics, Artificial Intelligence Journal, Machine Learning Journal, Journal of Artificial Intelligence Research, Communications of the ACM, Computational Intelligence Journal, International Journal of Intelligent Systems, Journal of the ACM, Journal of Logic and Computation, ACM Transactions on Programming Languages and Systems, ACM Transactions on Computer Systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Data and Knowledge Engineering, IEEE Transactions on Systems, Man and Cybernetics, Natural Hazards, Journal of Global Policy, Cognitive Science Journal.
Reviewer, ACM Dissertation Award, 1988
Reviewer, International Joint Conference on Artificial Intelligence, 1987, 1989, 1991, 1993, 1995; Conference on Uncertainty in AI, 1991, 1992; International Conference on Automata, Languages, and Programming, 1987; Symposium on Parallel and Distributed Programming, 1993; Fifth Generation Computer Systems, 1992.
Book Reviewer, MIT Press, Pitman Press, Addison-Wesley, Morgan Kaufmann, Cambridge University Press, Oxford University Press, Elsevier

University Service

Computer Science Division

EECS Department

Other departments

College of Engineering

University

UC Systemwide

Publications

1. Stuart Russell The Use of Knowledge in Analogy and Induction. London: Pitman, 1989.
2. Stuart Russell and Eric H. Wefald Do the Right Thing: Studies in Limited Rationality. Cambridge, MA: MIT Press, 1991.
3. Stuart Russell and Peter Norvig Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice Hall, 1995.
4. Stuart Russell and Peter Norvig Solution Manual for ``Artificial Intelligence: A Modern Approach.'' Englewood Cliffs, NJ: Prentice Hall, 1995.
5. Armand Prieditis and Stuart Russell (Eds.), Machine Learning: Proceedings of the Twelfth International Conference, Tahoe City, CA: Morgan Kaufmann, 1995.
6. Stuart Russell and Peter Norvig, Inteligencia Artificial: Un Enfoque Moderno (R. Gutierrez, Tr.). Mexico City: Prentice Hall Hispanoamericana, 1997.
7. Stuart Russell and Peter Norvig, [Artificial Intelligence: A Modern Approach] (in Japanese; Tr. Koichi Furukawa et al.). Tokyo: Kyoritsu Shuppan, 1997.
8. Stuart Russell and Peter Norvig, Intelligenza Artificiale: Un Approccio Moderno (L. Aiello, Tr.). Turin, Italy: UTET Libreria Srl, 1998.
9. Stuart Russell and Peter Norvig, Mesterséges Intelligencia Modern Megközelitésben. (Hungarian translation of Artificial Intelligence: A Modern Approach.) Budapest, Panem Publishing, 1999.
10. Stuart Russell and Peter Norvig Artificial Intelligence: A Modern Approach (Second Edition). Upper Saddle River, NJ: Prentice Hall, 2003.
11. Stuart Russell and Peter Norvig, ``Solution Manual for Artificial Intelligence: A Modern Approach.'' 2nd Edition, Prentice Hall, 2003.
12. Stuart Russell and Peter Norvig, Inteligencia Artificial. (Portuguese translation of Artificial Intelligence: A Modern Approach, second edition.) Rio de Janeiro: Elsevier/Editora Campus, 2004.
13. Stuart Russell and Peter Norvig, 《人工智能——一种现代方法(第二版)》 (Chinese Simplified translation of Artificial Intelligence: A Modern Approach, second edition.) Beijing: Posts and Telecommunications Press, 2004.
14. Stuart Russell and Peter Norvig, Inteligencia Artificial: Un Enfoque Moderno. (Spanish translation of Artificial Intelligence: A Modern Approach, second edition.) Madrid: Pearson Educación, S. A., 2004.
15. Stuart Russell and Peter Norvig, Τεχνητή Νοημοσύνη, Μια σύγχρονη προσέγγιση. (Greek translation of Artificial Intelligence: A Modern Approach, second edition.) Athens: Kleidarithmos, 2004.
16. Stuart Russell and Peter Norvig, Intelligenza Artificiale Vol. 1 Un Approccio Moderno. (Vol. 1 of Italian translation of Artificial Intelligence: A Modern Approach, second edition.) Rome: Pearson Italia, 2005.
17. Stuart Russell and Peter Norvig, Künstliche Intelligenz: Ein moderner Ansatz (2nd Edition). (German translation of Artificial Intelligence: A Modern Approach, second edition.) Munich: Verlag Pearson Studium, 2005.
18. Stuart Russell and Peter Norvig, Искусственный интеллект: современный подход. (Russian translation of Artificial Intelligence: A Modern Approach, second edition.) Translated by Konstantin Ptitsyn. Moscow: Williams Publishing, 2005.
19. Stuart Russell and Peter Norvig, Mesterséges Intelligencia Modern Megközelitésben. (Hungarian translation of Artificial Intelligence: A Modern Approach, second edition.) Budapest, Panem Publishing, 2006.
20. Stuart Russell and Peter Norvig, Intelligenza Artificiale Vol. 2 Un Approccio Moderno. (Vol. 2 of Italian translation of Artificial Intelligence: A Modern Approach, second edition.) Rome: Pearson Italia, 2006.
21. Stuart Russell and Peter Norvig, Intelligence artificielle, 2e éd.. (French translation of Artificial Intelligence: A Modern Approach, second edition.) Paris: Pearson Education France, 2006.
22. Stuart Russell and Peter Norvig, (Japanese translation of Artificial Intelligence: A Modern Approach, second edition.) Koichi Furukawa, translator. Tokyo: Kyoritsu Shuppan, 2008.
23. Stuart Russell and Peter Norvig, ``Artificial Intelligence: A Modern Approach.'' 3rd Edition, Prentice Hall, 2010.
24. Stuart Russell and Peter Norvig, Intelligence artificielle, 3e éd.. (French translation of Artificial Intelligence: A Modern Approach, third edition.) Paris: Pearson Education France, 2010.
25. Stuart Russell and Peter Norvig, Intelligenza Artificiale 3/Ed. Vol. 1 - Un Approccio Moderno. (Vol. 1 of Italian translation of Artificial Intelligence: A Modern Approach, third edition.) Rome: Pearson Italia, 2010.
26. Stuart Russell and Peter Norvig, Veštačkka inteligencija, Savremeni pristup. (Serbian translation of Artificial Intelligence: A Modern Approach, third edition.) Belgrade, CET, 2011.
27. Stuart Russell and Peter Norvig, Künstliche Intelligenz: Ein moderner Ansatz (3rd Edition). (German translation of Artificial Intelligence: A Modern Approach, third edition.) Munich: Verlag Pearson Studium, 2012.
28. Stuart Russell and Peter Norvig, 《人工智能——一种现代方法(第三版)》 (Chinese Simplified translation of Artificial Intelligence: A Modern Approach, third edition.) Tsinghua University Press, 2013.
29. Stuart Russell and Peter Norvig, Жасанды интеллект: Жаңашыл әдіс. (Kazakh translation of Artificial Intelligence: A Modern Approach, third edition.) Translated by M. E. Mansurova, K. S. Duisebekova, and S. Z. Sapakova. Almaty, Kazakhstan: Association of Higher Education Institutions of the Republic of Kazakhstan, 2013, 2014, 2016.
30. Stuart Russell and Peter Norvig, 인공지능 : 현대적 접근방식(제 3판), Volume 1 and Volume 2. (Korean translation of Artificial Intelligence: A Modern Approach, third edition.) Seoul: J-pub Co., 2016.
31. Stuart Russell, Human Compatible: AI and the Problem of Control . New York: Viking, 2019.
32. Stuart Russell and Peter Norvig, ``Artificial Intelligence: A Modern Approach.'' 4th Edition, Pearson, 2020.
33. Stuart Russell, Human Compatible: Künstliche Intelligenz und wie der Mensch die Kontrolle über superintelligente Maschinen behält. (German translation of Human Compatible.) Translated by Guido Lenz. Cologne: mitp Verlag, 2020.
34. Stuart Russell, AI新生. (Chinese translation of Human Compatible.) Translated by Zhang Yi. Beijing: CITIC Press, 2020.
35. Stuart Russell, Сумісний з людиною. Штучний інтелект і проблема контролю. (Ukrainian translation of Human Compatible.) Translated by Viktoria Zenhyea. Kiev: Bookchef, 2020.
36. Stuart Russell, Совместимость: Как контролировать искусственный интеллект. (Russian translation of Human Compatible.) Moscow: Alpina, 2020.
37. Stuart Russell, AI新生. (Japanese translation of Human Compatible.) Translated by Nobuhiko Matsui. Tokyo: Misuzu Shobo, 2021.
38. Stuart Russell, 어떻게 인간과 공존하는 인공지능을 만들 것인가. (Korean translation of Human Compatible.) Translated by Lee Han-eum. Seoul: Gimm-Young, 2021.
39. Stuart Russell, İnsanlık için Yapay Zekâ: Yapay Zekâ ve Kontrol Problemi. (Turkish translation of Human Compatible.) Translated by Barış Satılmış. Ankara: Buzdağı Yayınevi, 2021.
40. Stuart Russell, Συμβατή με τον άνθρωπο. (Greek translation of Human Compatible.) Athens: Travlos Publications, 2021.
41. Stuart Russell, Inteligência Artificial a Nosso Favor: como manter o controle sobre a tecnologia. (Portuguese translation of Human Compatible.) Translated by Berila Vargas and illustrated by Mateus Valadares. São Paulo, Brazil: Companhia das Letras.
42. Stuart Russell, Jako člověk: Umělá inteligence a problém jejího ovládání. (Croatian translation of Human Compatible.) Translated by Jiří Zlatuška. Zagreb, Croatia: Planetopija.
43. Stuart Russell and Peter Norvig, Intelligence artificielle, 4e éd.. (French translation of Artificial Intelligence: A Modern Approach, fourth edition.) Paris: Pearson Education France, 2022.
44. Stuart Russell, Human Compatible (revised edition). London: Penguin, 2023.

Journal papers

1. Stuart Russell ``Rationality as an Explanation of Language?'' (commentary). Behavioral and Brain Sciences 10, 1987.
2. Stuart Russell and Eric Wefald ``Principles of Metareasoning.'' Artificial Intelligence 49, 1991 (invited paper).
3. Stuart Russell ``An Architecture for Bounded Rationality.'' SIGART Bulletin 2(4), 1991.
4. Stuart Russell ``Prior Knowledge and Autonomous Learning.'' Journal of Robotics and Autonomous Systems 8, 1991.
5. Stuart Russell ``Inductive Learning by Machines.'' Philosophical Studies 64(1), 1991.
6. Stuart Russell and Devika Subramanian ``Provably bounded-optimal agents.'' Journal of Artificial Intelligence Research, 2, 1995 (invited paper).
7. Shlomo Zilberstein and Stuart Russell ``Optimal composition of real-time systems.'' Artificial Intelligence, 82, 181-213, 1996.
8. Stuart Russell, ``Rationality and Intelligence.'' Artificial Intelligence, 94, 57-77, 1997 (invited paper).
9. John Binder, Daphne Koller, Stuart Russell, Keiji Kanazawa, ``Adaptive Probabilistic Networks with Hidden variables.'' Machine Learning, 29, 213-244, 1997 (invited paper).
10. Stuart Russell, Lewis Stiller, and Othar Hansson, ``PNPACK: Computing with Probabilities in Java.'' Concurrency: Practice and Experience, 9, 1333-1339, 1997.
11. Prasad Tadepalli and Stuart Russell, ``Learning from Examples and Membership Queries with Structured Determinations.'' Machine Learning, 32, 245-95, 1998.
12. Tim Huang and Stuart Russell, ``Object Identification: A Bayesian Analysis with Application to Traffic Surveillance.'' Artificial Intelligence, 103, 1-17, 1998 (invited paper).
13. Geoffrey Zweig and Stuart Russell, ``Probabilistic modeling with Bayesian networks for automatic speech recognition.'' Australian Journal of Intelligent Information Processing Systems, 5(4), 253-60, 1999 (invited paper).
14. Songhwai Oh, Stuart Russell, and S. Shankar Sastry, ``Markov Chain Monte Carlo Data Association for Multi-Target Tracking.'' IEEE Transactions on Automatic Control, 54(3), 481-497, 2009.
15. Ahilan Sivaganesan, Yusuf Erol, Geoffrey Manley, and Stuart Russell, ``Modeling and Machine Learning of Cerebrovascular Dynamics: A Framework for Monitoring Unmeasurable Patient Variables.'' Neurosurgery, 71(2), E559, 2012.
16. Nimar S. Arora, Stuart Russell, and Erik Sudderth, ``NET-VISA: Network Processing Vertically Integrated Seismic Analysis.'' In Bulletin of the Seismological Society of America, 103(2A), 709-729, 2013.
17. Stuart Russell, Unifying logic and probability. Communications of the ACM, 58(7), 88-97, 2015.
18. Stuart Russell, Daniel Dewey, and Max Tegmark, Research Priorities for Robust and Beneficial Artificial Intelligence, AI Magazine, Vol. 36, No. 4, 2015.
19. Hugh Chen, Yusuf Erol, Eric Shen, and Stuart Russell, Probabilistic model-based approach for heart beat detection.'' Physiological Measurement, 37(9), 1404-1421, 2016.
20. Ronan Le Bras, Nimar Arora, Noriyuki Kushida, Pierrick Mialle, Istvan Bondar, Elena Tomuta, Fekadu Kebede Alamneh, Paulino Feitio, Marcela Villarroel, Beatriz Vera, Alexander Sudakov, Shaban Laban, Stuart Nippress, David Bowers, Stuart Russell, and Tammy Taylor, ``NET-VISA from Cradle to Adulthood. A Machine-Learning Tool for Seismo-Acoustic Automatic Association,'' Pure and Applied Geophysics, DOI 10.1007/s00024-020-02508-x, 2020.
21. Paria Rashidinejad, Xiao Hu, and Stuart Russell, Patient-adaptable intracranial pressure morphology analysis using a probabilistic model-based approach.'' Physiological Measurement, 41(10), 104003.
22. Stuart Russell, The history and future of AI, Oxford Review of Economic Policy, 37(3), 509-520, 2021.
23. Stuart Russell, Banning Lethal Autonomous Weapons: An Education. Issues in Science and Technology, XXXVIII(3), 60-65, 2022.
24. Stuart Russell, If we succeed. Daedalus, Spring 2022, 43-57.
25. Kenji Doya, Arisa Ema, Hiroaki Kitano, Masamichi Sakagami, and Stuart Russell, Social impact and governance of AI and neurotechnologies. Neural Networks, 152, 542-554, 2022.
26. Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, and Stuart Russell, Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. IEEE Transactions on Information Theory, DOI: 10.1109/TIT.2022.3185139, 2022.
27. Alistair Knott, Dino Pedreschi, Raja Chatila, Tapabrata Chakraborti, Susan Leavy, Ricardo Baeza-Yates, David Eyers, Andrew Trotman, Paul D. Teal, Przemyslaw Biecek, Stuart Russell, and Yoshua Bengio, Generative AI models should include detection mechanisms as a condition for public release. Ethics and Information Technology, 25, 2023. (prepublication version)

Refereed conference papers in published proceedings

1. Stuart Russell ``A Quantitative Analysis of Analogy by Similarity.'' In Proceedings of the Fifth National Conference on Artificial Intelligence, Philadelphia, PA: Morgan Kaufmann, 1986.
2. Stuart Russell ``Preliminary Steps Toward the Automation of Induction.'' In Proceedings of the Fifth National Conference on Artificial Intelligence, Philadelphia, PA: Morgan Kaufmann, 1986.
3. Stuart Russell ``Analogy and Single-Instance Generalization.'' In Proceedings of the Fourth International Machine Learning Workshop, Irvine, CA: Morgan Kaufmann, 1987.
4. Todd R. Davies and Stuart Russell ``A Logical Approach to Reasoning by Analogy.'' In Proceedings of the Tenth International Joint Conference on Artificial Intelligence, Milan, Italy: Morgan Kaufmann, 1987.
5. Stuart Russell and Benjamin Grosof ``A Declarative Approach to Bias in Concept Learning.'' In Proceedings of the Sixth National Conference on Artificial Intelligence, Seattle, WA: Morgan Kaufmann, 1987.
6. Michael Braverman and Stuart Russell ``Boundaries of Operationality.'' In Proceedings of the Fifth International Conference on Machine Learning, Ann Arbor, MI: Morgan Kaufmann, 1989.
7. Michael Braverman and Stuart Russell ``IMEX: Overcoming Intractability in Explanation-Based Learning.'' In Proceedings of the Seventh National Conference on Artificial Intelligence, Minneapolis, MN: Morgan Kaufmann, 1988.
8. Stuart Russell ``Tree-Structured Bias.'' In Proceedings of the Seventh National Conference on Artificial Intelligence, Minneapolis, MN: Morgan Kaufmann, 1988.
9. Alice Agogino, Ramanathan Guha and Stuart Russell ``Sensor Fusion using Influence Diagrams and Reasoning by Analogy: Application to Milling Machine Monitoring and Control.'' In Proceedings of the Third International Conference on Applications of Artificial Intelligence in Engineering, Stanford, CA: Computational Mechanics Institute, 1988.
10. Stuart Russell and Eric Wefald ``Principles of Meta-Reasoning.'' In Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning, Toronto, Ontario: Morgan Kaufmann, 1989.
11. Stuart J. Russell ``Execution architectures and compilation.'' In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, MI: Morgan Kaufmann, 1989.
12. Stuart Russell and Eric Wefald ``On optimal game-tree search using rational metareasoning.'' In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, MI: Morgan Kaufmann, 1989.
13. Eric Wefald and Stuart Russell ``Adaptive Learning of Decision-Theoretic Search Control Knowledge.'' In Proceedings of the Sixth International Workshop on Machine Learning, Ithaca, NY: Morgan Kaufmann, 1989.
14. Benjamin Grosof and Stuart Russell ``Declarative Bias for Structural Domains.'' In Proceedings of the Sixth International Workshop on Machine Learning, Ithaca, NY: Morgan Kaufmann, 1989.
15. Sampath Srinivas, Stuart Russell, and Alice Agogino, ``Automated construction of sparse Bayesian networks from unstructured probabilistic models and domain information.'' In Proceedings of the Fifth Workshop on Uncertainty in Artificial Intelligence, Windsor, Ontario, 1989.
16. Stuart Russell ``Fine-grained decision-theoretic search control.'' In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Cambridge, MA: Morgan Kaufmann, 1990.
17. Stuart Russell and Shlomo Zilberstein ``Composing Real-Time Systems.'' In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, Sydney, Australia: Morgan Kaufmann, 1991.
18. Shlomo Zilberstein and Stuart Russell ``Efficient Resource-Bounded Reasoning in AT-RALPH.'' In Proceedings of the First International Conference on AI Planning Systems, College Park, Maryland: Morgan Kaufmann, 1992.
19. Ronald Musick and Stuart Russell ``How Long Will It Take?'' In Proceedings of the Tenth National Conference on Artificial Intelligence, San Jose, CA: AAAI Press, 1992.
20. Saso Dzeroski, Stephen Muggleton and Stuart Russell ``PAC-Learnability of Determinate Logic Programs.'' In Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory (COLT-92), Pittsburgh, PA: ACM Press, 1992.
21. Stuart Russell ``Efficient Memory-Bounded Search Methods.'' In Proceedings of the Tenth European Conference on Artificial Intelligence, Vienna: Wiley, 1992.
22. Saso Dzeroski, Stephen Muggleton and Stuart Russell ``PAC-Learnability of Constrained, Nonrecursive Logic Programs.'' In Proceedings of the Third International Workshop on Computational Learning Theory and Natural Learning Systems (CLNL-92), Madison, WI, 1992.
23. Musick, R., Catlett, J. and Russell, S. ``An Efficient Method for Constructing Approximate Decision Trees for Large Databases.'' In Proceedings of the Tenth International Conference in Machine Learning, Amherst, MA, 1993.
24. Gary Ogasawara and Stuart Russell ``Planning Using Multiple Execution Architectures.'' In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Chambery, France: Morgan Kaufmann, 1993.
25. Stuart Russell and Devika Subramanian ``Provably bounded optimal agents.'' In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Chambery, France, 1993.
26. S. Zilberstein and S. J. Russell. ``Anytime Sensing, Planning and Action: A Practical Model for Robot Control.'' In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Chambery, France, 1993.
27. T. Huang, D. Koller, J. Malik, G. Ogasawara, B. Rao, S. Russell, and J. Weber. ``Automatic symbolic traffic scene analysis using belief networks.'' In Proceedings of the Twelfth National Conference on Artificial Intelligence, Seattle, WA, 1994.
28. J. Tash and S. Russell ``Control strategies for a stochastic planner.'' In Proceedings of the Twelfth National Conference on Artificial Intelligence, Seattle, WA, 1994.
29. D. Koller, J Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russell, ``Towards Robust Automatic Traffic Scene Analysis in Real-Time.'' In Proceedings of the International Conference on Pattern Recognition, Israel, Nov. 1994.
30. D. Koller, J Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russell, ``Towards robust automatic traffic scene analysis in real-time.'' In Proceedings of the 33rd IEEE Conference on Decision and Control, Lake Buena Vista, Florida, Dec. 1994: IEEE Press.
31. Stuart Russell, John Binder, Daphne Koller, and Keiji Kanazawa, ``Local learning in probabilistic networks with hidden variables.'' In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Canada, 1995.
32. Ron Parr and Stuart Russell, ``Approximating Optimal Policies for Partially Observable Stochastic Domains.'' In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Canada, 1995.
33. Jeff Forbes, Tim Huang, Keiji Kanazawa, and Stuart Russell, ``The BATmobile: Towards a Bayesian Automated Taxi.'' In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Canada, 1995.
34. Stuart Russell, ``Rationality and Intelligence.'' Invited paper (Computers and Thought Award), in Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Canada, 1995.
35. Keiji Kanazawa, Daphne Koller, and Stuart Russell, ``Stochastic simulation algorithms for dynamic probabilistic networks.'' In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, Canada: Morgan Kaufmann, 1995.
36. M. Wellman, C. Liu, D. Pynadath, S. Russell, J. Forbes, T. Huang, and K. Kanazawa. ``Decision-Theoretic Reasoning for Traffic Monitoring and Vehicle Control.'' In Proceedings of the Intelligent Vehicles '95 Symposium, Detroit, Michigan, September 1995.
37. Timothy Huang and Stuart Russell, ``Object identification in a Bayesian context.'' Distinguished Paper Prize, in Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, Nagoya, Japan, 1997.
38. John Binder, Kevin Murphy, Stuart Russell, ``Space-Efficient Inference in Dynamic Probabilistic Networks.'' In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, Nagoya, Japan, 1997.
39. Nir Friedman, Moises Goldszmidt, David Heckerman, Stuart Russell, ``Where is the Impact of Bayesian Networks in Learning?'' In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, Nagoya, Japan, 1997.
40. Nir Friedman and Stuart Russell, ``Image Segmentation in Video Sequences.'' In Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, Providence, Rhode Island: Morgan Kaufmann, 1997.
41. Ron Parr and Stuart Russell, ``Reinforcement Learning with Hierarchies of Machines.'' In Advances in Neural Information Processing Systems 10, MIT Press, 1998.
42. Nir Friedman, Kevin Murphy, and Stuart Russell, ``Learning the Structure of Dynamic Probabilistic Networks.'' In Proceedings of the Conference on Automated Learning and Discovery, Pittsburgh, June 1998.
43. Geoff Zweig and Stuart Russell, ``Speech Recognition with Dynamic Bayesian Networks.'' In Proceedings of the Fifteenth National Conference on Artificial Intelligence, Madison, Wisconsin: AAAI Press, 1998.
44. R. Dearden, N. Friedman, and S. Russell, ``Bayesian Q-Learning.'' In Proceedings of the Fifteenth National Conference on Artificial Intelligence, Madison, Wisconsin: AAAI Press, 1998.
45. N. Friedman, K. Murphy, and S. Russell, ``Learning the Structure of Dynamic Probabilistic Networks.'' In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, Wisconsin: Morgan Kaufmann, 1998.
46. S. Russell, ``Learning agents for uncertain environments (extended abstract).'' Invited paper, in Proceedings of the Eleventh Annual ACM Workshop on Computational Learning Theory (COLT-98), Madison, Wisconsin: ACM Press, 1998.
47. G. Zweig and S. Russell, ``Probabilistic Modeling with Bayesian Networks for ASR.'' In Proceedings of the International Conference on Spoken Language Processing, Sydney, Australia: IEEE Press, 1998.
48. Andrew Y. Ng, Daishi Harada, and Stuart Russell, ``Policy invariance under reward transformations: Theory and application to reward shaping.'' In Proceedings of the Sixteenth International Conference on Machine Learning, Bled, Slovenia: Morgan Kaufmann, 1999.
49. Vassilis Papavassiliou and Stuart Russell, ``Convergence of reinforcement learning with general function approximators.'' In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, Stockholm: Morgan Kaufmann, 1999.
50. Hanna Pasula, Stuart Russell, Michael Ostland, and Ya'acov Ritov, ``Tracking many objects with many sensors.'' In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, Stockholm: Morgan Kaufmann, 1999.
51. Stuart Russell, ``Expressive probability models for speech recognition and understanding.'' In Proc. International Workshop on Automatic Speech Recognition and Understanding (ASRU), Keystone, Colorado, 1999 (invited paper).
52. Stuart Russell, ``Expressive probability models in science.'' In Proceedings of the Second International Conference on Discovery Science, Tokyo, Japan: Springer Verlag, 1999 (invited paper).
53. Andrew Y. Ng and Stuart Russell, ``Algorithms for inverse reinforcement learning.'' In Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, California: Morgan Kaufmann, 2000.
54. Arnaud Doucet, Nando de Freitas, Kevin Murphy, and Stuart Russell, ``Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks.'' In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence , Stanford, California: Morgan Kaufmann, 2000.
55. David Andre and Stuart Russell, ``Programmable Reinforcement Learning Agents.'' In Advances in Neural Information Processing Systems 13, MIT Press, 2001.
56. Nikunj Oza and Stuart Russell, ``Online Bagging and Boosting.'' In Eighth International Workshop on Artificial Intelligence and Statistics, Key West, Florida. 2001
57. Nikunj Oza and Stuart Russell, ``Experimental Comparisons of Online and Batch Versions of Bagging and Boosting.'' In Proc. KDD-01, San Francisco, 2001.
58. Stuart Russell, ``Identity uncertainty.'' In Proc. IFSA-01, Vancouver, 2001. (Invited paper)
59. Joao de Freitas, Pedro Hoejen-Soerensen, Michael Jordan, and Stuart Russell, ``Variational MCMC.'' In Proc. UAI-01, Seattle, 2001.
60. Hanna Pasula and Stuart Russell, ``Approximate inference for first-order probabilistic languages.'' In Proc. IJCAI-01, Seattle, 2001.
61. Bhaskara Marthi, Hanna Pasula, Stuart Russell, Yuval Peres, ``Decayed MCMC Filtering.'' In Proc. UAI-02, Edmonton, Alberta: Morgan Kaufmann, 2002.
62. David Andre and Stuart Russell, ``State Abstraction for Programmable Reinforcement Learning Agents.'' In Proc. AAAI-02, Edmonton, Alberta: AAAI Press, 2002.
63. Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, and Stuart Russell, ``Distance Metric Learning, with application to Clustering with side-information.'' In Advances in Neural Information Processing Systems 15, MIT Press, 2003.
64. Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart Russell, and Ilya Shpitser, ``Identity Uncertainty and Citation Matching.'' In Advances in Neural Information Processing Systems 15, MIT Press, 2003.
65. Eric P.Xing, Michael I. Jordan, Richard M. Karp, and Stuart Russell, ``A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences.'' In Advances in Neural Information Processing Systems 15, MIT Press, 2003.
66. Eyal Amir and Stuart Russell, ``Temporal Logical Filtering--Preliminary Results.'' In Proc. Common Sense 2003, Stanford, CA, 2003.
67. Eyal Amir and Stuart Russell, ``Logical Filtering.'' In Proc. IJCAI-03, Acapulco, Mexico, 2003.
68. Stuart Russell and Andrew Zimdars, ``Q-Decomposition for Reinforcement Learning Agents.'' In Proc. ICML-03, Washington, DC, 2003.
69. Greg Lawrence, Noah Cowan, and Stuart Russell, ``Efficient Gradient Estimation for Motor Control Learning.'' In Proc. UAI-03, Acapulco, Mexico, 2003.
70. Eric P. Xing, Michael I. Jordan, and Stuart Russell, ``A generalized mean field algorithm for variational inference in exponential families.'' In Proc. UAI-03, Acapulco, Mexico, 2003.
71. Eric Xing, Michael Jordan, and Stuart Russell, ``Graph partition strategies for generalized mean field inference.'' In Proc. UAI-04, Banff, Alberta, 2004.
72. Songhwai Oh, Stuart Russell, and Shankar Sastry, ``Markov Chain Monte Carlo Data Association for General Multiple Target Tracking Problems.'' In Proc. 43rd IEEE Conference on Decision and Control (CDC-04), Paradise Island, Bahamas, 2004.
73. Brian Milch, Bhaskara Marthi, David Sontag, Stuart Russell, Daniel L. Ong and Andrey Kolobov, ``Approximate Inference for Infinite Contingent Bayesian Networks.'' In Proc. Tenth International Workshop on Artificial Intelligence and Statistics, Barbados, 2005.
74. Brian Milch, Bhaskara Marthi, David Sontag, Stuart Russell, Daniel L. Ong and Andrey Kolobov, ``BLOG: Probabilistic Models with Unknown Objects.'' In Proc. IJCAI-05, Edinburgh, Scotland, 2005.
75. Stuart Russell and Jason Wolfe, ``Efficient belief-state AND-OR search, with application to Kriegspiel.'' In Proc. IJCAI-05, Edinburgh, Scotland, 2005.
76. Bhaskara Marthi, Stuart Russell, David Latham, and Carlos Guestrin, ``Concurrent hierarchical reinforcement learning.'' In Proc. IJCAI-05, Edinburgh, Scotland, 2005.
77. Brian Milch and Stuart Russell, ``General-Purpose MCMC Inference over Relational Structures.'' In Proc. UAI-06, Cambridge, MA, 2006.
78. Bhaskara Marthi and Stuart Russell, ``A Compact, Hierarchical Q-Function Decomposition.'' In Proc. UAI-06, Cambridge, MA, 2006.
79. T. K. Satish Kumar and Stuart Russell, ``On Some Tractable Cases of Logical Filtering.'' In Proceedings of the Sixteenth International Conference on Automated Planning and Scheduling (ICAPS 2006), Ambleside, UK, 2006.
80. Brian Milch and Stuart Russell, ``First-Order Probabilistic Languages: Into the Unknown.'' In ILP: Proceedings of the 16th International Conference on Inductive Logic Programming. Berlin: Springer, 2007.
81. B. Marthi, S. J. Russell, and J. Wolfe, ``Angelic Semantics for High-Level Actions.'' In Proceedings of the Seventeenth International Conference on Automated Planning and Scheduling (ICAPS 2007), Providence, Rhode Island, 2007.
82. Gregory Lawrence and Stuart Russell, ``Improving Gradient Estimation by Incorporating Sensor Data.'' In Proceedings of the 24th International Conference on Uncertainty in Artificial Intelligence (UAI-08), Helsinki, 2008.
83. Bhaskara Marthi, Stuart Russell, and Jason Wolfe, ``Angelic Hierarchical Planning: Optimal and Online Algorithms.'' In Proceedings of the Eighteenth International Conference on Automated Planning and Scheduling (ICAPS 2008), Sydney, 2008.
84. Norm Aleks, Stuart Russell, Michael G. Madden, Kristan Staudenmayer, Mitchell Cohen, Diane Morabito, and Geoffrey Manley, ``Probabilistic detection of short events, with application to critical care monitoring.'' In Advances in Neural Information Processing Systems 21, MIT Press, 2009.
85. Emma Brunskill and Stuart Russell, ``RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains.'' In Proc. 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), Catalina Island, California, 2010.
86. Nimar Arora, Stuart Russell, Rodrigo de Salvo Braz, and Erik Sudderth, ``Gibbs sampling in open-universe stochastic languages.'' In Proc. 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), Catalina Island, California, 2010.
87. Shaunak Chatterjee and Stuart Russell, ``Why are DBNs sparse?.'' In Proc. Thirteenth International Conference on Artificial Intelligence and Statistics, Sardinia, 2010.
88. Ronan Le Bras, Sheila Vaidya, Jeffrey Schneider, Stuart Russell, and Nimar Arora, ``Status of the Machine Learning Efforts at the International Data Centre of the CTBTO.'' In Proc. Monitoring Research Review (MRR 2010), Orlando, Florida, 2010.
89. Jason Wolfe, Bhaskara Marthi, and Stuart Russell, ``Combined Task and Motion Planning for Mobile Manipulation.'' In Proceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS 2010), Toronto, 2010.
90. Nimar S. Arora, Stuart J. Russell, Paul Kidwell, and Erik Sudderth, ``Global seismic monitoring as probabilistic inference.'' In Advances in Neural Information Processing Systems 23, MIT Press, 2011.
91. Ronan Le Bras, Stuart Russell, Nimar Arora, and Vera Miljanovic, ``Machine Learning at the CTBTO. Testing and evaluation of the False Events Identification (FEI) and Vertically Integrated Seismic Association (VISA) project.'' In Proc. Monitoring Research Review (MRR 2011), Tucson, Arizona, 2011.
92. Stuart J. Russell, Stephen C. Myers, Nimar S. Arora, David A. Moore, and Erik Sudderth, ``Bayesian Treaty Monitoring: Preliminary Report.'' In Proc. Monitoring Research Review (MRR 2011), Tucson, Arizona, 2011.
93. Shaunak Chatterjee and Stuart Russell, ``A temporally abstracted Viterbi algorithm.'' In Proc. UAI-11, Barcelona, 2011.
94. Nimar S. Arora, Stuart J. Russell, Paul Kidwell, and Erik Sudderth, ``Global seismic monitoring: A Bayesian approach.'' In Proc. AAAI-11, San Francisco, 2011.
95. Jason Wolfe and Stuart Russell, ``Bounded Intention Planning.'' In Proc. IJCAI-11, Barcelona, 2011.
96. Emma Brunskill and Stuart Russell, ``Partially observable sequential decision making for problem selection in an intelligent tutoring system.'' In Proc. International Conference on Educational Data Mining (EDM), 2011.
97. Nicholas Hay, Stuart Russell, Solomon Eyal Shimony, and David Tolpin, ``Selecting Computations: Theory and Applications.'' In Proc. UAI-12, Catalina Island, 2012.
98. Shaunak Chatterjee and Stuart Russell, ``Uncertain observation times.'' In Proc. 6th International Conference on Scalable Uncertainty Management (SUM-12), Marburg, Germany, 2012.
99. David A. Moore, Kevin Mayeda, Steve Myers, Min Joon Seo, and Stuart Russell, ``Progress in Signal-Based Bayesian Monitoring.'' In Proc. Monitoring Research Review (MRR 2012), Albuquerque, New Mexico, 2012.
100. Nimar S. Arora, Jeffrey Given, Elena Tomuta, Stuart Russell, and Spilios Spiliopoulos, ``Analyst Evaluation of NET-VISA (Network Processing Vertically Integrated Seismic Analysis) at the CTBTO.'' In Proc. Monitoring Research Review (MRR 2012), Albuquerque, New Mexico, 2012.
101. Lei Li, Bharath Ramsundar, and Stuart Russell, ``Dynamic Scaled Sampling for Deterministic Constraints.'' In Proc. Sixteenth International Conference on Artificial Intelligence and Statistics, Scottsdale, Arizona, 2013.
102. Yusuf B. Erol, Lei Li, Bharath Ramsundar, and Stuart Russell, ``The Extended Parameter Filter.'' In Proc. Thirtieth International Conference on Machine Learning, Atlanta, 2013.
103. Sharad Vikram, Lei Li, and Stuart J. Russell, ``Writing and sketching in the air, recognizing and controlling on the fly.'' In CHI Extended Abstracts, Paris, 2013.
104. Mark Rogers, Lei Li, and Stuart Russell, ``Multilinear Dynamical Systems for Tensor Time Series.'' In Advances in Neural Information Processing Systems 23, MIT Press, 2014.
105. Siddharth Srivastava, Eugene Fang, Lorenzo Riano, Rohan Chitnis, Stuart Russell, and Pieter Abbeel, ``Combined Task and Motion Planning Through an Extensible Planner-Independent Interface Layer.'' In Proc. ICRA-14, Hong Kong, 2014.
106. Siddharth Srivastava, Stuart Russell, and Paul Ruan, ``First-Order Open-Universe POMDPs.'' In Proc. UAI-14, Quebec City, Canada, 2014.
107. David Moore and Stuart Russell, ``Fast Gaussian Process Posteriors with Product Trees.'' In Proc. UAI-14, Quebec City, Canada, 2014.
108. Stuart Russell, ``Unifying Logic and Probability: A New Dawn for AI?'' (Invited paper.) In Proc. IPMU-14, Montpellier, France, 2014.
109. Yusuf Erol, Romi Phadte, Sammy Sidhu, Claire Asselstine, David Phillips, and Stuart Russell, ``Model-Based Probabilistic Inference for Intensive Care Medicine.'' In Proc. Fifth Conference on Meaningful Use of Complex Medical Data, Los Angeles, 2015.
110. F. Lieder, D. Plunkett, J. Hamrick, S. Russell, N. Hay, T. Griffiths, Algorithm selection by rational metareasoning as a model of human strategy selection. In Advances in Neural Information Processing Systems 23, MIT Press, 2015.
111. Siddharth Srivastava, Shlomo Zilberstein, Abhishek Gupta, Pieter Abbeel, Stuart Russell, Tractability of Planning with Loops. In Proc. AAAI-15, Austin, Texas, 2015.
112. Dylan Hadfield-Menell and Stuart Russell, Multitasking: Efficient Optimal Planning for Bandit Superprocesses. In Proc. UAI-15, 2015.
113. Wei Wang and Stuart Russell, A Smart-Dumb/Dumb-Smart Algorithm for Efficient Split-Merge MCMC. In Proc. UAI-15, 2015.
114. David Moore and Stuart Russell, Gaussian Process Random Fields. In Advances in Neural Information Processing Systems 24, MIT Press, 2016.
115. Siddharth Srivastava, Stuart Russell, and Alessandro Pinto, Metaphysics of Planning Domain Descriptions. In Proc. AAAI-16, Phoenix, 2016.
116. Aijun Bai, Siddharth Srivastava, and Stuart Russell, Markovian State and Action Abstractions via Hierarchical MCTS. In Proc. IJCAI-16, New York, 2016.
117. Yi Wu, Lei Li, Stuart Russell, and Rastislav Bodik, Swift: Compiled Inference for Probabilistic Programming Languages. In Proc. IJCAI-16, New York, 2016.
118. Stuart Russell, Ole Torp Lassen, Justin Uang, and Wei Wang, The Physics of Text: Ontological Realism in Information Extraction. In Proc. AKBC-16 (Automated Knowledge Base Construction), San Diego, 2016.
119. Dylan Hadfield-Menell, Christopher Lin, Rohan Chitnis, Stuart Russell, and Pieter Abbeel, Sequential Quadratic Programming for Task Plan Optimization, In Proc. IROS-16, Daejeon, Korea, 2016.
120. Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel, and Stuart Russell, Cooperative Inverse Reinforcement Learning. In Proc. NeurIPS-16, Barcelona, 2016.
121. David Moore and Stuart Russell, ``Signal-Based Bayesian Seismic Monitoring.'' In Proc. Twentieth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, Florida, 2017.
122. Aijun Bai and Stuart Russell, ``Speeding up HAM learning with internal transitions.'' In Proc. Third Multidisciplinary Conference on Reinforcement Learning and Decision Making, Ann Arbor, Michigan, 2017.
123. Yusuf B. Erol, Yi Wu, Lei Li, and Stuart Russell, A Nearly-Black-Box Online Algorithm for Joint Parameter and State Estimation in Temporal Models. In Proc. AAAI-17, San Francisco, 2017.
124. Smitha Milli, Dylan Hadfield-Menell, Anca Dragan, and Stuart Russell, ``Should Robots be Obedient?.'' In Proc. IJCAI-17, Melbourne, 2017. Also on arXiv.
125. Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel, and Stuart Russell, ``The off-switch game.'' In Proc. IJCAI-17, Melbourne, 2017.
126. Aijun Bai and Stuart Russell, ``Efficient Reinforcement Learning with Hierarchies of Machines by Leveraging Internal Transitions.'' In Proc. IJCAI-17, Melbourne, 2017.
127. Yi Wu, David Bamman, and Stuart Russell, ``Adversarial Training for Relation Extraction,'' In Proc. EMNLP-17, Copenhagen, 2017.
128. Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart Russell, Anca Dragan, ``Inverse Reward Design.'' In Proc. NeurIPS-17, Long Beach, 2017.
129. Nicholas Hay, Siddharth Srivastava, Yi Wu, and Stuart Russell, ``Discrete-Continuous Mixtures in Probabilistic Programming: Extended Semantics and General Inference Algorithms.'' In Proc. ICML-18, Stockholm, 2018.
130. Malayandi Palaniappan, Dhruv Malik, Jaime Fisac, Dylan Hadfield-Menell, Anca Dragan, and Stuart Russell, ``An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning.'' In Proc. ICML-18, Stockholm, 2018.
131. Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart Russell, and Pieter Abbeel, ``Learning Plannable Representations with Causal InfoGAN.'' In Proc. NeurIPS-18, Montreal, 2018.
132. Tongzhou Wang, Yi Wu, David Moore, and Stuart Russell, ``Meta-Learning MCMC Proposals.'' In Proc. NeurIPS-18, Montreal, 2018.
133. Andrew Critch, Nishant Desai, and Stuart Russell, ``Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making.'' In Proc. NeurIPS-18, Montreal, 2018.
134. Shihui Li, Yi Wu, Xinyue Cui, Honghua Dong, Fei Fang, and Stuart Russell, ``Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient. In Proc. AAAI-19, Honolulu, 2019.
135. Joshua Peterson, David Bourgin, Daniel Reichman, Thomas Griffiths, and Stuart Russell, ``Cognitive model priors for predicting human decisions.'' In Proc. ICML-19, Long Beach, California, 2019. (See also video of presentation.)
136. Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, and Yuandong Tian, ``Bayesian Relational Memory for Semantic Visual Navigation.'' In Proc. ICCV-19, Seoul, 2019.
137. Adam Gleave, Michael Dennis, Neel Kant, Cody Wild, Sergey Levine and Stuart Russell, ``Adversarial Policies: Attacking Deep Reinforcement Learning.'' In Proc. ICLR-20, Addis Ababa, 2020.
138. Paria Rashidinejad, Jiantao Jiao, and Stuart Russell, ``SLIP: Learning to predict in unknown dynamical systems with long-term memory.'' In Advances in Neural Information Processing Systems 33, 2021. (Proceedings of NeurIPS-20.)
139. Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, and Sergey Levine, ``Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design.'' In Advances in Neural Information Processing Systems 33, 2021. (Proceedings of NeurIPS-20.)
140. Sam Toyer, Rohin Shah, Andrew Critch, and Stuart Russell, ``The MAGICAL Benchmark for Robust Imitation.'' In Advances in Neural Information Processing Systems 33, 2021. (Proceedings of NeurIPS-20.)
141. Prasad Tadepalli and Stuart Russell, ``PAC-Learning of Causal Trees with Latent Variables.'' In Proc. AAAI-21.
142. Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell and Jan Leike, ``Quantifying Differences in Reward Functions.'' In Proc. ICLR-21, 2021.
143. George Matheos, Alexander K. Lew, Matin Ghavamizadeh, Stuart Russell, Marco Cusumano-Towner, Vikash K. Mansinghka, ``Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models.'' In Proc. 3rd Symposium on Advances in Approximate Bayesian Inference (AABI), 2021.
144. Charlotte Roman, Michael Dennis, Andrew Critch, and Stuart Russell. Accumulating Risk Capital Through Investing in Cooperation. In Proc. AAMAS-21.
145. Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao, Yuandong Tian, Joseph E. Gonzalez, and Stuart Russell, MADE: Exploration via Maximizing Deviation from Explored Regions . In Advances in Neural Information Processing Systems 34, 2022. (Proceedings of NeurIPS-21.)
146. Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, and Stuart Russell, Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. In Advances in Neural Information Processing Systems 34, 2022. (Proceedings of NeurIPS-21.)
147. Cassidy Laidlaw and Stuart Russell, Uncertain Decisions Facilitate Better Preference Learning. In Advances in Neural Information Processing Systems 34, 2022. (Proceedings of NeurIPS-21.)
148. Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart Russell, and Noam Brown, Scalable Online Planning via Reinforcement Learning Fine-Tuning. In Advances in Neural Information Processing Systems 34, 2022. (Proceedings of NeurIPS-21.)
149. Cynthia Chen, Sam Toyer, Cody Wild, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven Wang, Ping Luo, Stuart Russell, Pieter Abbeel, and Rohin Shah, An Empirical Investigation of Representation Learning for Imitation. In Advances in Neural Information Processing Systems 34, 2022. (Proceedings of NeurIPS-21, Datasets and Benchmarks.)
150. Arnaud Fickinger, Samuel Cohen, Stuart Russell, and Brandon Amos, Cross-Domain Imitation Learning via Optimal Transport. In Proc. ICLR-22.
151. Scott Emmons, Caspar Oesterheld, Andrew Critch, Vincent Conitzer, and Stuart Russell, For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria. In Proc. ICML-22.
152. Micah Carroll, Dylan Hadfield-Menell, Stuart Russell, and Anca Dragan, Estimating and Penalizing Induced Preference Shifts in Recommender Systems. In Proc. ICML-22.
153. Samer B. Nashed, Justin Svegliato, Abhinav Bhatia, Stuart Russell, and Shlomo Zilberstein, Selecting the Partial State Abstractions of MDPs: A Metareasoning Approach with Deep Reinforcement Learning. In Proc. IROS-22
154. Alexander Lew, George Matheos, Matin Ghavamizadeh, Nishad Gothoskar, Stuart Russell, and Vikash Mansinghka, SMCP3: SMC with Probabilistic Program Proposals. In Proc. Twenty-Sixth International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 2023.
155. Paria Rashidinejad, Hanlin Zhu, Kunhe Yang, Stuart Russell, and Jiantao Jiao, Optimal conservative offline RL with general function approximation via augmented Lagrangian. In Proc. ICLR-23, 2023.
156. Joar Skalse, Matthew Farrugia-Roberts, Stuart Russell, Alessandro Abate, and Adam Gleave, Invariance in policy optimisation and partial identifiability in reward learning. In Proc. ICML-23, 2023.
157. Tony Tong Wang, Adam Gleave, Tom Tseng, Nora Belrose, Kellin Pelrine, Joseph Miller, Michael D Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, and Stuart Russell, Adversarial policies beat superhuman Go AIs. In Proc. ICML-23, 2023.
158. Niklas Lauffer, Ameesh Shah, Micah Carroll, Michael D Dennis, Stuart Russell, Who needs to know? Minimal knowledge for optimal coordination. In Proc. ICML-23, 2023.
159. Cassidy Laidlaw, Stuart Russell, and Anca Dragan, Bridging RL Theory and Practice with the Effective Horizon, In Proc. NeurIPS-23, 2023.

Refereed conference papers in non-archival proceedings

1. Stuart Russell and Eric Wefald ``Multi-Level Decision-Theoretic Search.'' In Proceedings of the AAAI Symposium on Computer Game-Playing, Stanford, March, 1988.
2. Michael Braverman and Stuart Russell ``Explanation-Based Learning in Complex Domains.'' In Proceedings of the AAAI Symposium on Explanation-Based Learning, Stanford, March, 1988.
3. Stuart Russell and Devika Subramanian ``Mutual Constraints on Representation and Inference.'' In Proceedings of the International Workshop on Machine Learning, Meta-Reasoning and Logics, Sesimbra, Portugal, 1988.
4. Stuart Russell and Benjamin Grosof ``A Sketch of Autonomous Learning using Declarative Bias.'' In Proceedings of the International Workshop on Machine Learning, Meta-Reasoning and Logics, Sesimbra, Portugal, 1988.
5. Benjamin Grosof and Stuart Russell ``Shift of Bias as Nonmonotonic Reasoning.'' In Proceedings of the International Workshop on Machine Learning, Meta-Reasoning and Logics, Sesimbra, Portugal, 1988.
6. Eric Wefald and Stuart Russell, ``Estimating the value of computation: The case of real-time search.'' In Proceedings of the AAAI Symposium on AI and Limited Rationality, Stanford, March, 1989.
7. Othar Hansson, Andrew Mayer, and Stuart Russell, ``Decision-theoretic planning in BPS.'' In Proceedings of the AAAI Symposium on Planning in Uncertain Environments, Stanford, March, 1990.
8. Stuart Russell ``An Architecture for Bounded Rationality.'' In Proceedings of the AAAI Symposium on Integrated Architectures for Intelligent Agents, Stanford, March, 1991.
9. Stuart Russell ``An Architecture for Bounded Rationality.'' In Proceedings of the IJCAI Workshop on Theoretical and Practical Design of Rational Agents, Sydney, August, 1991.
10. Shlomo Zilberstein and Stuart Russell ``Reasoning about optimal allocation of time using conditional performance profiles.'' In Proceedings of the AAAI-92 Workshop on Implementations of Temporal Reasoning, San Jose, CA, July, 1992.
11. Shlomo Zilberstein and Stuart Russell ``Control of Mobile Robots Using Anytime Computation.'' In Proceedings of the AAAI Fall Symposium on Applications of Artificial Intelligence to Real-World Autonomous Mobile Robots, Cambridge, MA, October, 1992.
12. Shlomo Zilberstein and Stuart Russell ``Constructing utility-driven real-time systems using anytime algorithms.'' Proceedings of the IEEE Workshop on Imprecise and Approximate Computation, Phoenix, AZ, December, 1992.
13. Gary Ogasawara and Stuart Russell ``Decision-theoretic planning with multiple execution architectures.'' Proceedings of the AAAI Spring Symposium on AI Planning, Stanford, CA, March 1993.
14. Stuart Russell and Devika Subramanian ``Constructing bounded optimal systems.'' In Proceedings of the AAAI Spring Symposium on AI and NP-hard problems, Stanford, CA, March 1993.
15. Brenda Barbour and Stuart Russell ``Experiments in Adaptive Indexing for Logic Programming.'' In Proceedings of the ICML-93 Workshop on Knowledge Compilation and Speedup Learning, Amherst, MA, June 1993.
16. Tim Huang, Gary Ogasawara, and Stuart Russell ``Symbolic Traffic Scene Analysis Using Belief Networks.'' In Proceedings of the AAAI Workshop on AI in Intelligent Vehicle and Highway Systems, Washington, DC, 1993.
17. Scott Davies and Stuart Russell, ``NP-completeness of searches for smallest possible feature sets.'' In Proceedings of the AAAI Fall Symposium on Relevance, New Orleans, Nov. 1994.
18. Stuart Russell and Peter Norvig, ``A Modern, Agent-Oriented Approach to AI Instruction.'' In Proceedings of the AAAI Fall Symposium on Innovative Instruction for Introductory AI, New Orleans, Nov. 1994.
19. Daishi Harada and Stuart Russell, ``Extended abstract: Learning search strategies.'' In Proceedings of the AAAI Spring Symposium on Search Techniques for Problem Solving under Uncertainty and Incomplete Information, Stanford, CA, 1999.
20. Michael Shilman, Hanna Pasula, Stuart Russell, and Richard Newton, ``Statistical Visual Language Models for Ink Parsing.'' In Proc. AAAI Spring Symposium on Sketch Understanding, Stanford, March 2002.
21. Bhaskara Marthi, Brian Milch, and Stuart Russell, ``First-Order Probabilistic Models for Information Extraction.'' In Proc. IJCAI-03 Workshop on Learning Statistical Models from Relational Data, Acapulco, Mexico, 2003.
22. Brian Milch, Bhaskara Marthi, and Stuart Russell, ``BLOG: Relational Modeling with Unknown Objects.'' In Proc. ICML-04 Workshop on Statistical Relational Learning, Banff, Canada, 2004.
23. Bhaskara Marthi, David Latham, Carlos Guestrin, Stuart Russell, ``Concurrent Hierarchical Reinforcement Learning.'' In Proc. AAAI-04 Workshop on Learning and Planning in Markov Processes, San Jose, 2004.
24. Bhaskara Marthi, Stuart Russell, and David Latham, ``Writing Stratagus-Playing Agents in Concurrent ALisp.'' In Proc. IJCAI-05 Workshop on Reasoning, Representation, and Learning in Computer Games, Edinburgh, Scotland, 2005.
25. Jason Wolfe and Stuart Russell, ``Exploiting Belief State Structure in Graph Search.'' In Proceedings of the ICAPS 2007 Workshop on Planning in Games, Providence, Rhode Island, 2007.
26. Norm Aleks, Stuart Russell, Michael G. Madden, Diane Morabito, Geoffrey Manley, Kristan Staudenmayer, and Mitchell Cohen, ``Probabilistic modeling of sensor artifacts in critical care,'' In Proc. ICML-08 Workshop on Machine Learning in Health Care Applications, Helsinki, 2008.
27. Rodrigo de Salvo Braz, Sriram Natarajan, Hung Bui, Jude Shavlik, and Stuart Russell, ``Anytime Lifted Belief Propagation,'' In Proc. SRL-2009, the International Workshop on Statistical Relational Learning, Leuven, Belgium, 2009.
28. Siddharth Srivastava and Stuart Russell, ``First-Order Models for POMDPs.'' In Proc. 2nd International Workshop on Statistical Relational AI (StarAI-12), Catalina Island, 2012.
29. Siddharth Srivastava, Lorenzo Riano, Stuart Russell, and Pieter Abbeel, ``Using Classical Planners for Tasks with Continuous Operators in Robotics.'' In ICAPS-13 Workshop on Planning and Robotics, Rome, 2013.
30. Yusuf B. Erol, Stuart J. Russell, Ahilan Sivaganesan, and Geoffrey T. Manley, ``Combined State and Parameter Estimation of Human Intracranial Hemodynamics.'' In Proc. NIPS-13 Workshop on Machine Learning for Clinical Data Analysis and Healthcare, Lake Tahoe, California, 2013.
31. David A. Moore, Kevin Mayeda, Stephen C. Myers, Stuart J. Russell, Bayesian Inference for Signal-Based Seismic Monitoring (poster), AGU Fall Meeting, San Francisco, 2015.
32. Stuart Russell, Nimar S. Arora, and David Moore, ``Bayesian Monitoring Systems for the CTBT: Historical Development and New Results.'' Eos Transactions of the American Geophysical Union, Fall Meeting Supplement, Abstract S34A-07, 2016.
33. David Moore and Stuart Russell, ``Initial Evaluation of Signal-Based Bayesian Monitoring.'' Eos Transactions of the American Geophysical Union, Fall Meeting Supplement, Abstract S31A-2707, 2016.
34. Nimar S. Arora and Stuart Russell, ``Fine-Scale Event Location and Error Analysis in NET-VISA.'' Eos Transactions of the American Geophysical Union, Fall Meeting Supplement, Abstract S31A-2710, 2016.
35. Malayandi Palaniappan, Dhruv Malik, Dylan Hadfield-Menell, Anca Dragan, Stuart Russell, ``Efficient Cooperative Inverse Reinforcement Learning.'' In Proc. ICML Workshop on Reliable Machine Learning in the Wild, 2017.
36. Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart Russell and Pieter Abbeel, ``Learning Plannable Representations with Causal InfoGAN.'' In Proc. ICML / IJCAI / AAMAS 2018 Workshop on Planning and Learning (PAL-18), Stockholm, 2018. Revised version on arXiv:1807.09341.
37. Aaron Tucker, Adam Gleave, and Stuart Russell, ``Inverse Reinforcement Learning for Video Games.'' In Proc. NeurIPS-18 Workshop on Deep RL, 2018.
38. Prasad Tadepalli, Cameron Barrie, and Stuart J. Russell, ``Learning Causal Trees with Latent Variables via Controlled Experimentation.'' In Proc. AAAI Spring Symposium on Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-Based AI, Stanford, 2019.
39. Adam Gleave, Michael Dennis, Neel Kant, Cody Wild, Sergey Levine and Stuart Russell, ``Adversarial Policies: Attacking Deep Reinforcement Learning.'' In Proc. ICML-19 Workshop on Security and Privacy in Machine Learning, Long Beach, California, 2019.
40. Rohin Shah, Pedro Freire, Neel Alex, Rachel Freedman, Dmitrii Krasheninnikov, Lawrence Chan, Michael Dennis, Pieter Abbeel, Anca Dragan and Stuart Russell, ``Benefits of Assistance over Reward Learning. In Proc. NeurIPS-20 Workshop on Cooperative AI. (Best Paper Prize)
41. Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell and Jan Leike, ``Quantifying Differences in Reward Functions. In Proc. NeurIPS-20 Workshop on Cooperative AI.
42. Arnaud Fickinger, Simon Zhuang, Andrew Critch, Dylan Hadfield-Menell and Stuart Russell, ``Multi-Principal Assistance Games: Definition and Collegial Mechanisms. In Proc. NeurIPS-20 Workshop on Cooperative AI.
43. Pedro Freire, Adam Gleave, Sam Toyer and Stuart Russell, ``DERAIL: Diagnostic Environments for Reward And Imitation Learning. In Proc. NeurIPS-20 Workshop on Cooperative AI.
44. Cassidy Laidlaw, Stuart Russell, and Anca Dragan, Bridging RL Theory and Practice with the Effective Horizon, ICML 2023 Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023. (Best Paper Award)
45. Sam Toyer, Olivia Watkins, Ethan Adrian Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, Alan Ritter, and Stuart Russell, Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game, NeurIPS Workshop on Robustness of Few-shot and Zero-shot Learning in Large Foundation Models, 2023.

Book chapters

1. Stuart Russell ``Analogy by Similarity.'' In David Helman (Ed.), Analogical Reasoning, Boston, MA: D. Reidel, 1988.
2. Stuart Russell and Benjamin Grosof ``Declarative Bias: An Overview.'' In Benjamin, P. (Ed.) Change of Representation and Inductive Bias, Dordrecht: Kluwer Academic Publishers, 1989.
3. Stuart Russell and Devika Subramanian ``Mutual Constraints on Representation and Inference.'' In Brazdil, P., and Konolige, K. (Eds.) Machine Learning, Meta-Reasoning and Logics. Dordrecht: Kluwer Academic Publishers, 1990.
4. Stuart Russell and Benjamin Grosof ``A Sketch of Autonomous Learning using Declarative Bias.'' In Brazdil, P., and Konolige, K. (Eds.) Machine Learning, Meta-Reasoning and Logics. Dordrecht: Kluwer Academic Publishers, 1990.
5. Benjamin Grosof and Stuart Russell ``Shift of Bias as Nonmonotonic Reasoning.'' In Brazdil, P., and Konolige, K. (Eds.) Machine Learning, Meta-Reasoning and Logics. Dordrecht: Kluwer Academic Publishers, 1990.
6. Stuart Russell ``Prior Knowledge and Autonomous Learning.'' In Maes, P. and van der Velde, W. (Eds.) Representation and Learning in an Autonomous Agent. Cambridge, MA: MIT Press, 1990.
7. Sampath Srinivas, Stuart Russell, and Alice Agogino, ``Automated construction of sparse Bayesian networks from unstructured probabilistic models and domain information.'' In M. Henrion, R. D. Shachter, L. N. Kanal, and J. F. Lemmer (Eds.) Uncertainty in Artificial Intelligence 5. Amsterdam: North Holland, 1990.
8. Todd R. Davies and Stuart Russell ``A Logical Approach to Reasoning by Analogy.'' In T. Dietterich (Ed.) Readings in Machine Learning. San Mateo, CA: Morgan Kaufmann, 1990.
9. Stuart Russell and Devika Subramanian ``On Provably RALPHs.'' In E. Baum (Ed.) Computational Learning and Cognition: Proceedings of the Third NEC Research Symposium. SIAM Press, 1993.
10. Saso Dzeroski, Stephen Muggleton and Stuart Russell ``PAC-Learnability of Constrained, Nonrecursive Logic Programs.'' In T. Petsche, S. Hanson, and J. Shavlik (Eds.), Computational Learning Theory and Natural Learning Systems, Volume III: Selecting Good Models, MIT Press, 1995.
11. Shlomo Zilberstein and Stuart Russell, ``Approximate reasoning using anytime algorithms.'' In S. Natarajan (Ed.) Imprecise and Approximate Computation, Kluwer Academic Publishers, Dordrecht, 1995.
12. Stuart Russell, ``Machine Learning.'' Chapter 4 of M. A. Boden (Ed.), Artificial Intelligence, Academic Press, 1996. Part of the Handbook of Perception and Cognition.
13. Michael Jordan and Stuart Russell, ``Computational Intelligence.'' In The MIT Encyclopedia of the Cognitive Sciences, MIT Press, 1999.
14. Stuart Russell, ``Metareasoning.'' In The MIT Encyclopedia of the Cognitive Sciences, MIT Press, 1999.
15. Kevin Murphy and Stuart Russell, ``Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks.'' In Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas and N.J. Gordon (eds), Springer-Verlag, 2001.
16. Stuart Russell, ``Rationality and Intelligence.'' In Renee Elio (Ed.), Common sense, reasoning, and rationality, Oxford University Press, 2002.
17. Judea Pearl and Stuart Russell, ``Bayesian Networks.'' In Michael A. Arbib, Ed., The Handbook of Brain Theory and Neural Networks, 2nd edition, MIT Press, 2003.
18. Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L. Ong and Andrey Kolobov, ``BLOG: Probabilistic Models with Unknown Objects.'' In L. Getoor and B. Taskar, Eds., Introduction to Statistical Relational Learning. Cambridge, MA: MIT Press, 2007.
19. Brian Milch and Stuart Russell, ``Extending Bayesian Networks to the Open-Universe Case.'' In Rina Dechter, Hector Geffner, and Joseph Y Halpern, Eds., Heuristics, Probability and Causality. A Tribute to Judea Pearl. College Publications, 2010.
20. J. Claude Hemphill III, Marco D. Sorani, Stuart Russell, and Geoffrey T. Manley, ``Medical informatics.'' In P. D. Le Roux, J. M. Levine, and W. A. Kofke (Eds.), Monitoring in Neurocritical Care. Philadelphia: Elsevier, 2010.
21. Stuart Russell, Will they make us better people?, in John Brockman, Ed., What to Think About Machines That Think, Harper Collins, 2015.
22. Stuart Russell, ``Rationality and Intelligence: A Brief Update.'' In Vincent C. Müller (ed.), Fundamental Issues of Artificial Intelligence (Synthese Library). Berlin: Springer, 2016.
23. Stuart Russell, Provably beneficial artificial intelligence. In The Next Step: Exponential Life, BBVA OpenMind, 2017.
24. Stuart Russell, ``The Purpose Put Into the Machine.'' In John Brockman, Ed., Possible Minds: 25 Ways of Looking at AI, Penguin Press, 2019.
25. Stuart Russell, ``Artificial intelligence: A binary approach.'' In S. Matthew Liao (Ed.), Ethics of Artificial Intelligence. Oxford: Oxford University Press, 2020.
26. Stuart Russell, ``Human-Compatible Artificial Intelligence.'' In Stephen Muggleton and Nick Chater (eds.), Human-Like Machine Intelligence, Oxford University Press, 2021.
27. Raja Chatila, Virginia Dignum, Michael Fisher, Fosca Gianotti, Katharina Morik, Stuart Russell, David Sadek, and Karen Yeung, ``Trustworthy AI.'' In Bertrand Braunschweig and Malik Ghallab (eds.), Reflections on Artificial Intelligence for Humanity, Springer, 2021.
28. Jocelyn Maclure and Stuart Russell, ``AI for Humanity: The Global Challenges.'' In Bertrand Braunschweig and Malik Ghallab (eds.), Reflections on Artificial Intelligence for Humanity, Springer, 2021.
29. Stuart Russell, Artificial Intelligence and the Problem of Control. In H. Werthner, E. Prem, E. A. Lee, and C. Ghezzi (eds), Perspectives on Digital Humanism, Springer, 2021.

Technical reports, non-refereed conference papers

1. Stuart Russell The Compleat Guide to MRS. Stanford University Computer Science Department Report No. STAN-CS-85-1080; June 1985 126pp. Also published as Stanford Knowledge Systems Laboratory Report No. KSL-85-12.
2. Todd R. Davies and Stuart Russell A Logical Approach to Reasoning by Analogy. SRI AI Center Technical Note 385; 1986.
3. Stuart Russell Analogical and Inductive Reasoning. Ph. D. Thesis, Stanford University Department of Computer Science, December 1986. Also appeared as Stanford University Computer Science Department Technical Report STAN-CS-87-1150.
4. Stuart Russell and Eric Wefald, Decision-Theoretic Search Control: General Theory and an Application to Game-Playing, Computer Science Division Technical Report 88/435, University of California, Berkeley, CA, 1988.
5. Alice Agogino, Ramanathan Guha and Stuart Russell ``Sensor Fusion using Influence Diagrams and Reasoning by Analogy: Application to Milling Machine Monitoring and Control.'' Working paper 88-0304-P, Department of Mechanical Engineering, University of California, Berkeley, CA, 1988.
6. Stuart Russell and Benjamin Grosof ``A Sketch of Autonomous Learning using Declarative Bias.'' Research report no. RC 14613 (#64812), IBM Research Division, 1989.
7. Benjamin Grosof and Stuart Russell ``Shift of Bias as Nonmonotonic Reasoning.'' Research report no. RC 14614 (#65293), IBM Research Division, 1989.
8. Benjamin Grosof and Stuart Russell ``Declarative Bias for Structural Domains.'' Research report no. RC 14620 (#64608), IBM Research Division, 1989.
9. Ann Nicholson and Stuart Russell, ``Techniques for Handling Inference Complexity in Dynamic Belief Networks.'' Technical report no. CS-93-31, Computer Science Department, Brown University, 1993.
10. Stuart Russell, John Binder, and Daphne Koller, ``Adaptive probabilistic networks.'' Technical report UCB//CSD-94-824, July 24, 1994.
11. Jeff Forbes, Tim Huang, Keiji Kanazawa, and Stuart Russell, ``The BATmobile: Towards a Bayesian Automated Taxi.'' In SAE Future Transportation Technology Conference, Costa Mesa, CA, August, 1995.
12. Jitendra Malik, Stuart Russell. ``Traffic Surveillance and Detection Technology Development: New Traffic Sensor Technology Final Report.'' California PATH Research Report UCB-ITS-PRR-97-6, Institute of Transportation Studies, University of California, Berkeley. January 1997.
13. Simon Kasif and Stuart Russell (Eds.), Proceedings of the AAAI Fall Symposium on Learning Complex Behaviours, Cambridge, Massachusetts: AAAI Press.
14. Jeffrey Forbes, Nikunj Oza, Ronald Parr, and Stuart Russell. ``Feasibility Study of Fully Automated Traffic Using Decision-Theoretic Control.'' California PATH Research Report UCB-ITS-PRR-97-18, Institute of Transportation Studies, University of California, Berkeley. April 1997.
15. Geoffrey Zweig and Stuart Russell. ``Compositional Modeling with DPNs.'' Technical Report No. UCB-CSD-97-970, Computer Science Division, University of California, Berkeley. December 1997.
16. Vassilis Papavassiliou and Stuart Russell, ``Value Determination with General Function Approximators.'' Technical Report CSD-98-1005, Computer Science Division, UC Berkeley, 1998.
17. David Andre and Stuart Russell, ``State Abstraction for Programmable Reinforcement Learning Agents.'' Technical Report CSD-01-1156, Computer Science Division, UC Berkeley, 2001.
18. Songhwai Oh, Stuart Russell, and Shankar Sastry, ``Markov Chain Monte Carlo Data Association for Multiple-Target Tracking,'' University of California, Berkeley, Technical Report UCB//ERL M05/19, June 2005.
19. B. Marthi, S. J. Russell, and J. Wolfe, ``Angelic Semantics for High-Level Actions,'' Tech. Rep. UCB/EECS-2007-89, EECS Department, University of California, Berkeley, July 2007.
20. Bhaskara Marthi, Stuart Russell, and Jason Wolfe, ``Angelic Hierarchical Planning: Optimal and Online Algorithms,'' Tech. Rep. UCB/EECS-2008-150, EECS Department, University of California, Berkeley, Dec 2008.
21. Stuart Russell, Sheila Vaidya, ``Machine Learning and Data Mining for Comprehensive Test Ban Treaty Monitoring.'' Technical Report LLNL-TR-416780, Lawrence Livermore National Laboratory, 2009.
22. Stuart Russell, Nimar Arora, Michael Jordan, and Erik Sudderth, "Vertically Integrated Seismological Analysis I: Modeling." Eos Transactions of the American Geophysical Union, 90(52), Fall Meeting Supplement, Abstract S33D-08, 2009.
23. Nimar Arora, Stuart Russell, and Erik Sudderth, "Vertically Integrated Seismological Analysis II: Inference." Eos Transactions of the American Geophysical Union, 90(52), Fall Meeting Supplement, Abstract S31B-1713, 2009.
24. Nimar S. Arora, Stuart J. Russell, Paul Kidwell, and Erik Sudderth, ``Global seismic monitoring as probabilistic inference.'' Technical Report No. UCB/EECS-2010-108, EECS Department, University of California, Berkeley, 2010.
25. Stuart Russell, Sheila Vaidya, and Ronan Le Bras, ``Machine Learning for Comprehensive Nuclear-Test-Ban Treaty Monitoring.'' CTBTO Spectrum, 14, 32-35, 2010.
26. Nicholas J. Hay and Stuart Russell, ``Metareasoning for Monte Carlo Tree Search.'' Technical Report No. UCB/EECS-2011-119, EECS Department, University of California, Berkeley, 2010.
27. Nimar Arora, Tony Dear, and Stuart Russell, ``Scalable Probabilistic Inference for Global Seismic Monitoring.'' Eos Transactions of the American Geophysical Union, 92(53), Fall Meeting Supplement, Abstract S43B-2238, 2011.
28. Nimar Arora and Stuart Russell, ``A model of seismic coda arrivals to suppress spurious events (abstract).'' In Proc. European Geophysical Union General Assembly, Vienna, 2012.
29. Ahilan Sivaganesan, Yusuf Erol, Geoffrey Manley, and Stuart Russell, ``Modeling and Machine Learning of Cerebrovascular Dynamics: A Framework for Monitoring Unmeasurable Patient Variables (abstract).'' Congress of Neurological Surgeons Annual Meeting, Chicago, 2012.
30. Nimar Arora and Stuart Russell, ``A model of seismic coda arrivals to suppress spurious events (abstract).'' In Proc. European Geophysical Union General Assembly, Vienna, 2012.
31. Siddharth Srivastava, Xiang Cheng, Stuart J. Russell and Avi Pfeffer, ``First-Order Open-Universe POMDPs: Formulation and Algorithms.'' Technical Report No. UCB/2013-243, EECS Department, University of California, Berkeley, 2013.
32. Lei Li, Yi Wu and Stuart J. Russell, SWIFT: Compiled Inference for Probabilistic Programs. Technical Report EECS-2015-12, UC Berkeley, 2015.
33. Hugh Chen, Yusuf Erol, Eric Shen, Stuart Russell, Probabilistic Model-Based Approach for Heart Beat Detection. arXiv:1512.07931, December 2015.
34. Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel, and Stuart Russell, Cooperative Inverse Reinforcement Learning, arXiv:1606.03137, June 2016.
35. Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel, and Stuart Russell, The Off-Switch Game, arXiv:1611.08219, November 2016.
36. Tongzhou Wang, Yi Wu, David A. Moore, Stuart J. Russell, ``Neural Block Sampling,'' arXiv:1708.06040v1, August 2017.
37. Andrew Critch and Stuart Russell, ``Servant of Many Masters: Shifting priorities in Pareto-optimal sequential decision-making,'' arXiv:1711.00363, October 2017.
38. R. Le Bras, N. Arora, N. Kushida, P. Mialle, I. Bondar, S. Laban, M. Villarroel, B. Vera, A. Sudakov, S. Nippress, D. Bowers, S. Russell, and T. Taylor, ``The Machine-Learning Tool NET-VISA from Cradle to Adulthood - The Next Generation System of the IDC and the SnT Process.'' In CTBT: Science and Technology Conference, Vienna, 2019.
39. N. Arora, S. Russell, P. Mialle, R. Le Bras, and P. Nielsen, ``Recent Advances and Status of Generative Modeling for Network Processing at the CTBTO.'' In CTBT: Science and Technology Conference, Vienna, 2019.
40. Daniel Filan, Shlomi Hod, Cody Wild, Andrew Critch, and Stuart Russell, ``Pruned Neural Networks are Surprisingly Modular,'' arXiv:2003.04881, March 2020.
41. Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, and Jan Leike, ``Quantifying Differences in Reward Functions. arXiv:2006.13900, June 2020.
42. Rohin Shah, Cody Wild, Steven H. Wang, Neel Alex, Brandon Houghton, William Guss, Sharada Mohanty, Anssi Kanervisto, Stephanie Milani, Nicholay Topin, Pieter Abbeel, Stuart Russell, and Anca Dragan, ``The MineRL BASALT Competition on Learning from Human Feedback. arxiv.org/abs/2107.01969, July 2021.
43. Alexander Bustamante, Brandie M. Nonnecke, and Stuart Russell (Eds.), Responsible Artificial Intelligence: Recommendations to Guide the University of California's Artificial Intelligence Strategy. Final report of the University of California Presidential Working Group on AI. October 2021.
44. Stuart Russell and Daniel Susskind (Eds.), Positive AI Economic Futures, Insight Report, World Economic Forum, November 22, 2021.
45. Adam Gleave, Mohammad Taufeeque, Juan Rocamonde, Erik Jenner, Steven H. Wang, Sam Toyer, Maximilian Ernestus, Nora Belrose, Scott Emmons, and Stuart Russell, imitation: Clean Imitation Learning Implementations. arxiv.org:2211.11972, November 2022.
46. Tony T. Wang, Adam Gleave, Tom Tseng, Nora Belrose, Kellin Pelrine, Joseph Miller, Michael D Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell, Adversarial policies beat superhuman Go AIs. arXiv:2211.00241, February 2023.
47. Andrew Critch and Stuart Russell, TASRA: A Taxonomy and Analysis of Societal-Scale Risks from AI, arXiv:2306.06924, June 2023
48. Peter Barnett, Rachel Freedman, Justin Svegliato, and Stuart Russell, Active Reward Learning from Multiple Teachers. arXiv:2303.00894, March 2023.
49. Alistair Knott, Dino Pedreschi, Raja Chatila, Susan Leavy, Ricardo Baeza-Yates, Tapabrata Chakraborti, David Eyers, Andrew Trotman, Lama Saouma, Virginia Morini, Valentina Pansanella, Paul D. Teal, Przemyslaw Biecek, Ivan Bratko, Stuart Russell, and Yoshua Bengio, State-of-the-art Foundation AI Models Should be Accompanied by Detection Mechanisms as a Condition of Public Release, Report, Global Partnership on Artificial Intelligence, 2023.
50. Luke Bailey, Euan Ong, Stuart Russell, and Scott Emmons, Image Hijacks: Adversarial Images can Control Generative Models at Runtime, arXiv:2309.00236, September 2023.
51. Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atilim Gunes Baydin, Sheila McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca Dragan, Philip Torr, Stuart Russell, Daniel Kahnemann, Jan Brauner, and Soren Mindermann, Managing AI Risks in an Era of Rapid Progress, October 24, 2023.
52. Edmund Mills, Shiye Su, Stuart Russell, and Scott Emmons, ALMANACS: A Simulatability Benchmark for Language Model Explainability, arXiv:2312.12747, December 2023.

Articles in magazines, newspapers, blogs, etc.

1. Stephen Hawking, Stuart Russell, Max Tegmark, and Frank Wilczek, ``Transcending Complacency on Superintelligent Machines.'' Huffington Post, April 19, 2014.
2. Stuart Russell, ``Transcendence: An AI Researcher Enjoys Watching His Own Execution.'' Huffington Post, April 29, 2014.
3. Stuart Russell, Of Myths and Moonshine, contribution to the conversation on The Myth of AI on edge.org.
4. Stuart Russell, Take a stand on AI weapons. Nature, 521(7553), May 28, 2015.
5. Stuart Russell and others, Autonomous Weapons: an Open Letter from AI & Robotics Researchers, July 28, 2015.
6. Stuart Russell, Max Tegmark, and Toby Walsh, Why We Really Should Ban Autonomous Weapons: A Response [to Ackerman], IEEE Spectrum, August 3, 2015.
7. Stuart Russell, Hiroshima et Nagasaki: Des leçons pour le présent et pour l'avenir, Le Monde, August 7, 2015.
8. Stuart Russell, Max Tegmark, and Toby Walsh, Why we really should ban autonomous weapons: A response [to Wallace], Kurzweil AI Blog, August 10, 2015.
9. Stuart Russell, Ban lethal autonomous weapons, Boston Globe, September 8, 2015.
10. Stuart Russell, Moral Philosophy Will Become Part of the Tech Industry, Time, September 15, 2015.
11. Stuart Russell, Tom Dietterich, Eric Horvitz, Bart Selman, Francesca Rossi, Demis Hassabis, Shane Legg, Mustafa Suleyman, Dileep George, and Scott Phoenix, Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter, AI Magazine, Vol. 36, No. 4, 2015.
12. Eric Eaton, Tom Dietterich, Maria Gini, Barbara J. Grosz, Charles L. Isbell, Subbarao Kambhampati, Michael Littman, Francesca Rossi, Stuart Russell, Peter Stone, Toby Walsh, and Michael Wooldridge, Who speaks for AI?, AI Matters, Volume 2 Issue 2, December 2015, Pages 4-14.
13. Stuart Russell, Robots in war: the next weapons of mass destruction?, World Economic Forum, January 17, 2016.
14. Stuart Russell, Should we fear supersmart robots?. In Scientific American, 314, 58-59, June 2016.
15. Stuart Russell, Tiptoe through the nuclear minefield, in Nature, November 11, 2016.
16. Allan Dafoe and Stuart Russell, Yes, We Are Worried About the Existential Risk of Artificial Intelligence. MIT Technology Review, Nov 2, 2016.
17. Stuart Russell, ``Artificial intelligence: The future is superintelligent.'' (Review of Life 3.0 by Max Tegmark.) Nature, 548, 520-521, 2017. (31 August 2017)
18. Slaughterbots, short film. Released Nov 13, 2017.
19. Stuart Russell, ``The new weapons of mass destruction?,'' Security Times, February 2018, 40-41. (Special issue of The German Times for the 54th Munich Security Conference.)
20. Stuart Russell, Anthony Aguirre, Ariel Conn, and Max Tegmark, ``Why You Should Fear 'Slaughterbots' - A Response [to Scharre].'' IEEE Spectrum, January 23, 2018.
21. Stuart Russell, ``How to make AI that works, for us, BBC Science Focus, November 16, 2018.
22. Olaf Groth, Mark Nitzberg, and Stuart Russell, AI algorithms need FDA-style drug trials, Wired, August 15, 2019.
23. Stuart Russell, How to Stop Superhuman A.I. Before It Stops Us, New York Times, October 8, 2019.
24. Stuart Russell, Many Experts Say We Shouldn't Worry About Superintelligent AI. They're Wrong, IEEE Spectrum, October 8, 2019.
25. Ronald Arkin, Leslie Kaelbling, Stuart Russell, Dorsa Sadigh, Paul Scharre, Bart Selman, and Toby Walsh, A Path Towards Reasonable Autonomous Weapons Regulation, IEEE Spectrum, October 21, 2019.
26. Stuart Russell and Charles-Édouard Bouée, ``The secret to designing a positive future with AI? Imagination.'' World Economic Forum, October 30, 2020.
27. Stuart Russell, Anthony Aguirre, Emilia Javorsky and Max Tegmark, Lethal Autonomous Weapons Exist; They Must Be Banned, IEEE Spectrum, June 16, 2021.
28. Stuart Russell, It's time to ban autonomous killer robots before they become a threat, Financial Times, August 5, 2021.
29. Stuart Russell, Why we need to regulate non-state use of arms. Global Agenda, World Economic Forum, 18 May 2022.
30. Stuart Russell, Politicians must prepare for AI or face the consequences. The House (magazine of the UK Houses of Parliament), Oct 14, 2022.
31. Stuart Russell, AI weapons: Russia's war in Ukraine shows why the world must enact a ban. Nature, 614, 620-623, 2023. (21 February, 2023)
32. Stuart Russell, AI has much to offer humanity. It could also wreak terrible harm. It must be controlled.. The Guardian, April 2, 2023.
33. Stuart Russell, How can humans maintain control over AI, forever?, Boston Globe, May 15, 2023.
34. Stuart Russell, Opening statement for the hearing on Oversight of AI: Principles for Regulation, U.S. Senate Committee on the Judiciary, Subcommittee on Privacy, Technology, and the Law, Washington, DC, July 25, 2023.
35. Stuart Russell, Written testimony for the hearing on Oversight of AI: Principles for Regulation, U.S. Senate Committee on the Judiciary, Subcommittee on Privacy, Technology, and the Law, Washington, DC, July 25, 2023.
36. Stuart Russell, Karine Perset, and Marko Grobelnik, Updates to the OECD's definition of an AI system explained, OECD AI Policy Observatory, November 29, 2023.
37. Stuart Russell, Written testimony for the United States Senate AI Forum on Risk, Alignment, & Guarding Against Doomsday Scenarios, Washington, DC, December 6, 2023.
38. Stuart Russell, Jonathan Reckford, Amy Brachio, Atle Hoie, Jane Nelson, Peter Maurer, Badr Jafar, Jack Hidary It's not all doom and gloom: 8 experts on their reasons to be optimistic in 2024, Davos Agenda, World Economic Forum, January 12, 2024.

Theses supervised

1. desJardins, Marie PAGODA: A Model for Autonomous Learning in Probabilistic Domains. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 1992.
2. Zilberstein, Shlomo Operational Rationality Through Compilation of Anytime Algorithms. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 1993.
3. Ogasawara, Gary RALPH-MEA: A Real-Time, Decision-Theoretic Agent Architecture. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 1994.
4. Musick, Charles Ronald Belief network induction. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 1994.
5. Mayer, Andrew Rational Search. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 1994.
6. Tash, Jonathan Decision Theory Made Tractable: The Value of Deliberation, with Applications to Markov Decision Process Planning. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 1996.
7. Zweig, Geoff, Speech recognition using dynamic Bayesian networks. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 1998.
8. Parr, Ronald, Solution methods for large Markov decision processes. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 1998.
9. Hansson, Othar, Bayesian Problem-Solving Applied to Scheduling. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 1998.
10. Huang, Timothy, Probabilistic Methods for Intelligent Transportation Systems. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 1999.
11. Oza, Nikunj, Online Ensemble Learning. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2001.
12. Forbes, Jeffrey, Learning Optimal Control for Autonomous Vehicles. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2002.
13. Murphy, Kevin, Dynamic Bayesian Networks: Representation, Inference, and Learning. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2002.
14. Andre, David, State Abstraction for Programmable Reinforcement Learning Agents. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2003.
15. Pasula, Hanna, Identity Uncertainty. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2003.
16. Papavassiliou, Vassilis, Value Determination with Function Approximation. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2003.
17. Paskin, Mark, Exploiting Locality in Probabilistic Inference. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2004.
18. Xing, Eric, Probabilistic Graphical Models and Algorithms for Genomic Analysis. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2004.
19. Milch, Brian, Probabilistic Models with Unknown Objects. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2006.
20. Marthi, Bhaskara, Concurrent Hierarchical Reinforcement Learning. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2006.
21. Lawrence, Gregory, Efficient Gradient Estimation for Reinforcement Learning Algorithms. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2009.
22. Canini, Kevin, Nonparametric Bayesian Models of Categorization. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2011.
23. Wolfe, Jason, Optimal Hierarchical Planning. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2011.
24. Arora, Nimar, Model-based Bayesian Seismic Monitoring. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2012.
25. Chatterjee, Shaunak, Efficient inference algorithms for near-deterministic systems. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2013.
26. Hay, Nicholas, Principles of Metalevel Control. PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2016.
27. Moore, David, Signal-Based Bayesian Seismic Monitoring, PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2017.
28. Wu, Yi, On Building Generalizable Learning Agents, PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2019.
29. Shah, Rohin, Extracting and Using Preference Information from the State of the World, PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2020.
30. Kurutach, Thanard, Learning, Planning, and Acting with Models, PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2021.
31. Hadfield-Menell, Dylan, The Principal-Agent Alignment Problem in Artificial Intelligence, PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2021.
32. Rashidinejad, Paria, Reliable Prediction and Decision-Making in Sequential Environments, PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2022.
33. Gleave, Adam, Towards Trustworthy Machine Learning, PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2022.
34. Dennis, Michael, On Problem Specification and Self-Referential Claims, PhD thesis, Computer Science Division, University of California, Berkeley, CA, 2022.

MS Theses

1. Dutta, Soumitra Inductive Learning of Rules of Determination. MS report, Computer Science Division, University of California, Berkeley, CA, 1987.
2. Guha, Ramanathan Induction and Analogy in Engineering Expert Systems. MS report, Mechanical Engineering Department, University of California, Berkeley, CA, 1987.
3. Barbour, Brenda Experiments on Indexing Schemes for Logic Programming. MS report, Computer Science Division, University of California, Berkeley, CA, 1987.
4. Sampath Srinivas Creating Influence Diagrams from Examples. MS report, Mechanical Engineering Department, University of California, Berkeley, CA, 1988.
5. Wefald, Eric The Expected Value of Search: A Decision-Theoretic Framework for Game-Playing Algorithms. MS report, Computer Science Division, University of California, Berkeley, CA, 1988.
6. Malone, Christopher Planning, Execution and Knowledge Compilation in Real-Time Agents. MS report, Computer Science Division, University of California, Berkeley, CA, 1988.
7. Getoor, Lise Learning efficiently using declarative bias. MS report, Computer Science Division, University of California, Berkeley, CA, 1989.
8. Ogasawara, Gary A Distributed Decision-Theoretic Control System for a Mobile Robot. MS report, Computer Science Division, University of California, Berkeley, CA, 1989.
9. Koenig, Sven Optimal Probabilistic and Decision-Theoretic Planning using Markovian Decision Theory. MS report, Computer Science Division, University of California, Berkeley, CA, 1991.
10. Sarkar, Sudeshna Constructive Induction for Situated Agents. MS report, Computer Science Division, University of California, Berkeley, CA, 1991.
11. Conroy, Jeffrey Decision-Theoretic Control of Search in Probabilistic Domains. MS report, Computer Science Division, University of California, Berkeley, CA, 1991.
12. Marx, Sonia Using tree-structured bias in training neural networks. MS report, Computer Science Division, University of California, Berkeley, CA, 1991.
13. Glesner, Sabine Constructing flexible dynamic belief networks from first-order probabilistic knowledge bases. MS thesis, Computer Science Division, University of California, Berkeley, CA, 1994.
14. Zweig, Geoff, A comparison between dynamic belief networks and hidden Markov models. MS thesis, Computer Science Division, University of California, Berkeley, CA, 1996.
15. Braverman, Michael, Caste; A Class System for Tcl. MS thesis, Computer Science Division, University of California, Berkeley, CA, 1997.
16. Oza, Nikunj, Probabilistic Models of Driver Behavior. MS thesis, Computer Science Division, University of California, Berkeley, CA, 1998.
17. Zimdars, Andrew, Additive Value Function Decomposition for Reinforcement Learning Agents. MS thesis, Computer Science Division, University of California, Berkeley, CA, 2004.
18. Pearson, Mark, Utility-Directed Sampling in Influence Diagrams. MS thesis, Computer Science Division, University of California, Berkeley, CA, 2006.
19. Canini, Kevin, Modeling Categorization as a Dirichlet Process Mixture, MS thesis, Computer Science Division, University of California, Berkeley, CA, 2007.
20. Duckworth, Daniel, Monte Carlo Methods for Multiple Target Tracking and Parameter Estimation, MS thesis, Computer Science Division, University of California, Berkeley, CA, 2012.
21. Berzan, Constantin, Monte Carlo Methods for SLAM with Data Association Uncertainty, MS thesis, Computer Science Division, University of California, Berkeley, CA, 2015.
22. Zhou, Xiaofei, Coarse-to-fine MCMC in a seismic monitoring system, MS thesis, Computer Science Division, University of California, Berkeley, CA, 2015.
23. Desai, Nishant, Uncertain Reward-Transition MDPs for Negotiable Reinforcement Learning, MS thesis, Computer Science Division, University of California, Berkeley, CA, 2018.
24. Rulison, Jared, Program Synthesis in BLOG, MS thesis, Computer Science Division, University of California, Berkeley, CA, 2018.
25. Kim, Kyungna, Arrhythmia Classification in Multi-Channel ECG Signals Using Deep Neural Networks, MS thesis, Computer Science Division, University of California, Berkeley, CA, 2018.
26. Kha, Benjamin, Policy Transfer Algorithms for Meta Inverse Reinforcement Learning, MS thesis, Computer Science Division, University of California, Berkeley, CA, 2019.
27. Tibbetts, Jake, Explainable Classification of Nuclear Facility Operational State Using Node and Region Importance for Sensor Networks, MS thesis, Computer Science Division, University of California, Berkeley, CA, 2021.

Consulting, short courses