4 papers
Inference Time Policy Optimization for Offline RL with Differentiable World Models
Rohan Deb, Stephen J. Wright, Arindam Banerjee
Offline Reinforcement Learning (RL) learns optimal policies from fixed datasets, training a policy once and deploying it at inference time without further refinement. Inspired by m…
Towards Weaker Variance Assumptions for Stochastic Optimization
Ahmet Alacaoglu, Yura Malitsky, Stephen J. Wright
We revisit a classical assumption for analyzing stochastic gradient algorithms where the squared norm of the stochastic subgradient (or the variance for smooth problems) is allowed…
Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity
Ahmet Alacaoglu, Donghwan Kim, Stephen J. Wright
We focus on constrained, -smooth, potentially stochastic and nonconvex-nonconcave min-max problems either satisfying -cohypomonotonicity or admitting a solution to the -we…
Complexity of Single Loop Algorithms for Nonlinear Programming with Stochastic Objective and Constraints
Ahmet Alacaoglu, Stephen J. Wright
We analyze the complexity of single-loop quadratic penalty and augmented Lagrangian algorithms for solving nonconvex optimization problems with functional equality constraints. We…