Some code snippets.

KEYSTONE-ML. A software framework designed to simplify the construction of large scale, end-to-end, machine learning pipelines. With Evan Sparks, Shivaram Venkataraman, Tomer Kaftan, and Mike Franklin. [Project Page]

ADCG. Alternating Descent Conditional Gradient Method for sparse inverse problems. With Nick Boyd and Geoff Schiebinger. [Julia Code] [report]

ADMM DECODE. A fast algorithm for decoding error correcting codes with linear programming. With Xishuo Liu, Siddharth Barman, and Stark Draper. [C Code] [report]

HOTTOPIXX. Fast, parallel non-negative matrix factorization via linear programming. With Victor Bittorf, Chris Re, and Joel Tropp. [C code] [report]

HOGWILD! Lock-free, parallel stochastic gradient descent. With Feng Niu, Chris Re, Steve Wright. [C code] [report]

JELLYFISH. Parallel code for matrix factorization. With Chris Re. [ C code] [report]

GROUSE. Stochastic gradient code for subspace tracking. This particular code runs a column-wise matrix completion demo. With Laura Balzano. [Matlab code] [report]

RANDOM KITCHEN SINKS. Code for generating random features for large scale learning problems. With Ali Rahimi. [Matlab code] [report]