2 citations · 2 across the 9 of their papers we have counts for
8 papers · 1 filter
An Information-Theoretic Definition for Open-Ended Learning
Wanqiao Xu, Yifan Zhu, Benjamin Van Roy
A growing body of work points to the great promise of AI systems that can continually expand their capabilities as they operate in an open-ended environment. But yet there is no co…
On Aligning Prediction Models with Clinical Experiential Learning: A Prostate Cancer Case Study
Jacqueline J. Vallon, William Overman, Wanqiao Xu +11
Over the past decade, the use of machine learning (ML) models in healthcare applications has rapidly increased. Despite high performance, modern ML models do not always capture pat…
Formalizing Learning from Language Feedback with Provable Guarantees
Wanqiao Xu, Allen Nie, Ruijie Zheng +3
Interactively learning from observation and language feedback is an increasingly studied area driven by the emergence of large language model (LLM) agents. Despite impressive empir…
Exploration Unbound
Dilip Arumugam, Wanqiao Xu, Benjamin Van Roy
A sequential decision-making agent balances between exploring to gain new knowledge about an environment and exploiting current knowledge to maximize immediate reward. For environm…
RLHF and IIA: Perverse Incentives
Wanqiao Xu, Shi Dong, Xiuyuan Lu +3
Existing algorithms for reinforcement learning from human feedback (RLHF) can incentivize responses at odds with preferences because they are based on models that assume independen…
Pearl: A Production-ready Reinforcement Learning Agent
Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari +12
Reinforcement learning (RL) is a versatile framework for optimizing long-term goals. Although many real-world problems can be formalized with RL, learning and deploying a performan…
Posterior Sampling for Continuing Environments
Wanqiao Xu, Shi Dong, Benjamin Van Roy
We develop an extension of posterior sampling for reinforcement learning (PSRL) that is suited for a continuing agent-environment interface and integrates naturally into agent desi…
Uniformly Conservative Exploration in Reinforcement Learning
Wanqiao Xu, Jason Yecheng Ma, Kan Xu +2
A key challenge to deploying reinforcement learning in practice is avoiding excessive (harmful) exploration in individual episodes. We propose a natural constraint on exploration -…