2 citations · 2 across the 5 of their papers we have counts for
6 papers
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…
TalkToAgent: A Human-centric Explanation of Reinforcement Learning Agents with Large Language Models
Haechang Kim, Hao Chen, Can Li +1
Explainable Reinforcement Learning (XRL) has emerged as a promising approach in improving the transparency of Reinforcement Learning (RL) agents. However, there remains a gap betwe…
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…
OptiChat: Bridging Optimization Models and Practitioners with Large Language Models
Hao Chen, Gonzalo Esteban Constante-Flores, Krishna Sri Ipsit Mantri +3
Optimization models have been applied to solve a wide variety of decision-making problems. These models are usually developed by optimization experts but are used by practitioners…
FaultExplainer: Leveraging Large Language Models for Interpretable Fault Detection and Diagnosis
Abdullah Khan, Rahul Nahar, Hao Chen +2
Machine learning algorithms are increasingly being applied to fault detection and diagnosis (FDD) in chemical processes. However, existing data-driven FDD platforms often lack inte…