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MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation
Wenzhuo Yang, Jia Li, Caiming Xiong +1
Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions. Despite being studied actively, existing optimization-based methods oft…
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards
Alexander Trott, Stephan Zheng, Caiming Xiong +1
While using shaped rewards can be beneficial when solving sparse reward tasks, their successful application often requires careful engineering and is problem specific. For instance…
The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation
Chih-Yao Ma, Zuxuan Wu, Ghassan AlRegib +2
As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making. Specifically, the Visi…
Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
Chih-Yao Ma, Jiasen Lu, Zuxuan Wu +4
The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments. This challenging task demands that the ag…
Interactive Agent Modeling by Learning to Probe
Tianmin Shu, Caiming Xiong, Ying Nian Wu +1
The ability of modeling the other agents, such as understanding their intentions and skills, is essential to an agent's interactions with other agents. Conventional agent modeling…
Multi-Hop Knowledge Graph Reasoning with Reward Shaping
Xi Victoria Lin, Richard Socher, Caiming Xiong
Multi-hop reasoning is an effective approach for query answering (QA) over incomplete knowledge graphs (KGs). The problem can be formulated in a reinforcement learning (RL) setup,…