5 citations · 22 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
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,…