||Stochastic (block) BFGS method for
solving the empirical risk minimization problem
with logistic loss and L2 regularizer. Related
||A suite of randomized methods for
inverting positive definite matrices implemented in
|Random Linear Lab
||A lab for testing and comparing
randomized methods for solving linear systems.
Implemented in MATLAB. Related
||A framework for communication-efficient
distributed optimization for machine learning.
Accelerated, Parallel and PROXimal coordinate
descent. This is an efficient C++ code based on this paper.
We also implement PCDM (parallel coordinate
descent), SDCA (stochastic dual coordinate ascent)
and AGD (Accelerated Gradient Descent).
Semi-stochastic gradient descent method for fast training of L2 regularized logistic regression. This is an efficient C++ code (can be called from MATLAB), based on this paper.
|Serial [1 5], parallel [2 3 4] and distributed [6 7] coordinate descent code for big data optimization. The parallel and distributed codes can solve LASSO instances with terabyte matrices and billions of features, and are scalable.|
Associate Professor (Reader)
Fellow in Mathematical Sciences