Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …

In this paper we provide new randomized algorithms with improved runtimes for solving
linear programs with two-sided constraints. In the special case of the minimum cost flow …

This survey highlights the recent advances in algorithms for numerical linear algebra that
have come from the technique of linear sketching, whereby given a matrix, one first …

Value function approximation has demonstrated phenomenal empirical success in
reinforcement learning (RL). Nevertheless, despite a handful of recent progress on …

We show how to approximate a data matrix A with a much smaller sketch~ A that can be
used to solve a general class of constrained k-rank approximation problems to within (1+ ε) …

In this paper we improve upon the running time for finding a point in a convex set given a
separation oracle. In particular, given a separation oracle for a convex set K⊂ R n that is …

We present an ̃O(m+n^1.5)-time randomized algorithm for maximum cardinality bipartite
matching and related problems (eg transshipment, negative-weight shortest paths, and …

We study Nyström type subsampling approaches to large scale kernel methods, and prove
learning bounds in the statistical learning setting, where random sampling and high …

First-order stochastic methods are the state-of-the-art in large-scale machine learning
optimization owing to efficient per-iteration complexity. Second-order methods, while able to …

Kernel methods provide a principled way to perform non linear, nonparametric learning.
They rely on solid functional analytic foundations and enjoy optimal statistical properties …