1 citations · 1 across the 9 of their papers we have counts for
5 papers · 1 filter
Log-Averaged Mirror Prox for Fast, Large-Scale Optimal Transport in Linear Space
Matthew X. Burns, Jiaming Liang
We propose Log-Averaged Mirror Prox (LAMP), a linear-space primal-dual method for large-scale optimal transport. LAMP implements primal mirror prox updates by tracking an averaged…
GPU-Accelerated First-Order Method with Randomized Sampling for Binary Integer Programs
Ningji Wei, Jiaming Liang
We present a scalable, GPU-accelerated algorithmic framework for large binary integer programs that operates end-to-end with minimal synchronization overhead. The proposed method c…
Oracle-based Uniform Sampling from Convex Bodies
Thanh Dang, Jiaming Liang
We propose new Markov chain Monte Carlo algorithms to sample a uniform distribution on a convex body . Our algorithms are based on the proximal sampler, which uses Gibbs samplin…
A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Design
Haydn Thomas Jones, Natalie Maus, Josh Magnus Ludan +9
AI-driven discovery can greatly reduce design time and enhance new therapeutics' effectiveness. Models using simulators explore broad design spaces but risk violating implicit cons…
Unifying restart accelerated gradient and proximal bundle methods
Jiaming Liang
This paper presents a novel restarted version of Nesterov's accelerated gradient method and establishes its optimal iteration-complexity for solving convex smooth composite optimiz…