3 papers
cs.LG2026
IFlowNets: Extending Generative Samplers to Learn Strategies in Incomplete Information Games
Conor M. Artman, Nicholas Di, Scott Perkins
While many algorithms blend reinforcement learning (RL) with counterfactual regret (CFR) methods to leverage tradeoffs in computational speed and performance, there are fewer inves…
math.OC2026
Operator Splitting with Hamilton-Jacobi-based Proximals
Nicholas Di, Eric C. Chi, Samy Wu Fung
Operator splitting algorithms are a cornerstone of modern first-order optimization, decomposing complex problems into simpler subproblems solved via proximal operators. However, mo…
math.OC2025
A Monte Carlo Approach for Nonsmooth Convex Optimization via Proximal Splitting Algorithms
Nicholas Di, Eric C. Chi, Samy Wu Fung
Operator splitting algorithms are a cornerstone of modern first-order optimization, relying critically on proximal operators as their fundamental building blocks. However, explicit…