5 papers
Rethinking Robustness: A New Approach to Evaluating Feature Attribution Methods
Panagiota Kiourti, Anu Singh, Preeti Duraipandian +2
This paper studies the robustness of feature attribution methods for deep neural networks. It challenges the current notion of attributional robustness that largely ignores the dif…
Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation
Chenyu Wang, Weichao Zhou, Shantanu Ghosh +2
Radiology report generation (RRG) has shown great potential in assisting radiologists by automating the labor-intensive task of report writing. While recent advancements have impro…
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning
Zijian Guo, Weichao Zhou, Wenchao Li
Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset. Current state-of-the-art approaches are based on supervised learning w…
Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment
Weichao Zhou, Wenchao Li
Many imitation learning (IL) algorithms use inverse reinforcement learning (IRL) to infer a reward function that aligns with the demonstration. However, the inferred reward functio…
HyQE: Ranking Contexts with Hypothetical Query Embeddings
Weichao Zhou, Jiaxin Zhang, Hilaf Hasson +2
In retrieval-augmented systems, context ranking techniques are commonly employed to reorder the retrieved contexts based on their relevance to a user query. A standard approach is…