While reinforcement learning (RL) achieves tremendous success in sequential decision-
making problems of many domains, it still faces key challenges of data inefficiency and the …

Reinforcement learning serves as a potent tool for modeling dynamic user interests within
recommender systems, garnering increasing research attention of late. However, a …

Aligning generative models with human preference via RLHF typically suffers from
overoptimization, where an imperfectly learned reward model can misguide the generative …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

Empowered by neural networks, deep reinforcement learning (DRL) achieves tremendous
empirical success. However, DRL requires a large dataset by interacting with the …

We consider off-policy evaluation (OPE) in Partially Observable Markov Decision Processes
(POMDPs), where the evaluation policy depends only on observable variables and the …

In applications of offline reinforcement learning to observational data, such as in healthcare
or education, a general concern is that observed actions might be affected by unobserved …

Off-policy evaluation (OPE) aims to estimate the return of a target policy using some pre-
collected observational data generated by a potentially different behavior policy. In many …

Reinforcement learning is an essential paradigm for solving sequential decision problems
under uncertainty. Despite many remarkable achievements in recent decades, applying …

In this paper, we study nonparametric estimation of instrumental variable (IV) regressions.
Recently, many flexible machine learning methods have been developed for instrumental …