2 citations · 2 across the 5 of their papers we have counts for
3 papers · 1 filter
DASH: Fast, Valid Counterfactuals for Deep Networks via Batched Directional Search
Shraman Pal, Gabriel Medeiros, Clayton Escouper das Chagas +1
Counterfactual explanations are most useful when they can be generated with low latency, remain close to the factual input, and satisfy input-domain, categorical, and actionability…
Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
Hao Chen, Chendi Qian, Christopher Morris +2
Exact solution of hard combinatorial optimization problems often relies on strong convex relaxations, but solving these relaxations repeatedly inside a branch-and-bound algorithm c…
SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems
Asha Ramanujam, Adam Elyoumi, Hao Chen +5
Most existing safe reinforcement learning (RL) benchmarks focus on robotics and control tasks, offering limited relevance to high-stakes domains that involve structured constraints…