1 citations · 1 across the 4 of their papers we have counts for
6 papers
ProRL: Effective Reinforcement Learning for Proactive Recommendation via Rectified Policy Gradient Estimation
Hongru Hou, Tiehua Mei, Denghui Geng +5
Proactive Recommender Systems (PRSs) aim to guide user preference shift toward target items by generating paths of intermediate recommendations. Reinforcement learning (RL) provide…
Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning
Ao Xu, Tieru Wu
Gromov--Wasserstein (GW) distances compare graphs, shapes, and point clouds through internal distances, without requiring a common coordinate system. This invariance is powerful, b…
Good Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement Learning
Tiehua Mei, Minxuan Lv, Leiyu Pan +5
Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in large language models but treats all correct solutions equally, potentially reinforcing flawed traces th…
Generally-Occurring Model Change for Robust Counterfactual Explanations
Ao Xu, Tieru Wu
With the increasing impact of algorithmic decision-making on human lives, the interpretability of models has become a critical issue in machine learning. Counterfactual explanation…
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space
Yukai Zhang, Ao Xu, Zihao Li +1
In the realm of Artificial Intelligence (AI), the importance of Explainable Artificial Intelligence (XAI) is increasingly recognized, particularly as AI models become more integral…
Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms
Ao Xu, Tieru Wu
Counterfactual explanation generation is a powerful method for Explainable Artificial Intelligence. It can help users understand why machine learning models make specific decisions…