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20242026
most citedTalkToAgent: A Human-centric Explanation of Reinforcement Learning Agents with Large Language Models

2 citations · 2 across the 5 of their papers we have counts for

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cs.LG2026

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…

cs.LG2026

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…