5 citations · 5 across the 3 of their papers we have counts for
4 papers
Decision-Focused On-Policy Learning for Contextual Linear Optimization with Partial Feedback
Wyame Benslimane, Tinghan Ye, Pascal Van Hentenryck +1
Decision-focused learning (DFL) trains predictive models by optimizing downstream decision quality rather than standalone prediction accuracy. For contextual linear optimization, m…
Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints
Hyungki Im, Wyame Benslimane, Paul Grigas
We study an extension of contextual stochastic linear optimization (CSLO) that, in contrast to most of the existing literature, involves inequality constraints that depend on uncer…
Self-Supervised Penalty-Based Learning for Robust Constrained Optimization
Wyame Benslimane, Paul Grigas
We propose a new methodology for parameterized constrained robust optimization, an important class of optimization problems under uncertainty, based on learning with a self-supervi…
Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations
Albert Wilcox, Ashwin Balakrishna, Jules Dedieu +3
Providing densely shaped reward functions for RL algorithms is often exceedingly challenging, motivating the development of RL algorithms that can learn from easier-to-specify spar…