1 citations · 1 across the 5 of their papers we have counts for
5 papers
Last-Iterate Convergence of Policy Dynamics in Zero-Sum Networked Separable Markov Games
Zailin Ma
Solving Nash equilibria for general multi-player Markov games is computationally intractable, while two-player zero-sum Markov games admit fast last-iterate policy-optimization met…
On the Generalization Properties of Learning the Random Feature Models with Learnable Activation Functions
Zailin Ma, Jiansheng Yang, Yaodong Yang
This paper studies the generalization properties of a recently proposed kernel method, the Random Feature models with Learnable Activation Functions (RFLAF). By applying a data-dep…
Falcon: Fast Visuomotor Policies via Partial Denoising
Haojun Chen, Minghao Liu, Chengdong Ma +8
Diffusion policies are widely adopted in complex visuomotor tasks for their ability to capture multimodal action distributions. However, the multiple sampling steps required for ac…
Learning Expressive Random Feature Models via Parametrized Activations
Zailin Ma, Jiansheng Yang, Yaodong Yang
The random feature (RF) method is a powerful kernel approximation technique, but it typically uses fixed activation functions, limiting its adaptability across diverse tasks. To ov…
Near-Optimal Last-iterate Convergence of Policy Optimization in Zero-sum Polymatrix Markov games
Zailin Ma, Jiansheng Yang, Zhihua Zhang
Computing approximate Nash equilibria in multi-player general-sum Markov games is a computationally intractable task. However, multi-player Markov games with certain cooperative or…