5 citations · 22 across the 11 of their papers we have counts for
11 papers
A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimization
Yuchen Zhu, Yufeng Zhang, Zhaoran Wang +2
This paper studies minimax optimization problems defined over infinite-dimensional function classes of overparameterized two-layer neural networks. In particular, we consider the m…
Can Large Language Models Play Games? A Case Study of A Self-Play Approach
Hongyi Guo, Zhihan Liu, Yufeng Zhang +1
Large Language Models (LLMs) harness extensive data from the Internet, storing a broad spectrum of prior knowledge. While LLMs have proven beneficial as decision-making aids, their…
What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization
Yufeng Zhang, Fengzhuo Zhang, Zhuoran Yang +1
In this paper, we conduct a comprehensive study of In-Context Learning (ICL) by addressing several open questions: (a) What type of ICL estimator is learned by large language model…
An Analysis of Attention via the Lens of Exchangeability and Latent Variable Models
Yufeng Zhang, Boyi Liu, Qi Cai +2
With the attention mechanism, transformers achieve significant empirical successes. Despite the intuitive understanding that transformers perform relational inference over long seq…
Federated Offline Reinforcement Learning
Doudou Zhou, Yufeng Zhang, Aaron Sonabend-W +3
Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive health…
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic
Yufeng Zhang, Siyu Chen, Zhuoran Yang +2
Actor-critic (AC) algorithms, empowered by neural networks, have had significant empirical success in recent years. However, most of the existing theoretical support for AC algorit…
Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation
Zhihan Liu, Yufeng Zhang, Zuyue Fu +2
In generative adversarial imitation learning (GAIL), the agent aims to learn a policy from an expert demonstration so that its performance cannot be discriminated from the expert p…
Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization
Zhuoran Yang, Yufeng Zhang, Yongxin Chen +1
We consider the optimization problem of minimizing a functional defined over a family of probability distributions, where the objective functional is assumed to possess a variation…
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
Yufeng Zhang, Qi Cai, Zhuoran Yang +2
Temporal-difference and Q-learning play a key role in deep reinforcement learning, where they are empowered by expressive nonlinear function approximators such as neural networks.…
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate
Yufeng Zhang, Qi Cai, Zhuoran Yang +1
Generative adversarial imitation learning (GAIL) demonstrates tremendous success in practice, especially when combined with neural networks. Different from reinforcement learning,…
Learning to Order Sub-questions for Complex Question Answering
Yunan Zhang, Xiang Cheng, Yufeng Zhang +5
Answering complex questions involving multiple entities and relations is a challenging task. Logically, the answer to a complex question should be derived by decomposing the comple…