3 papers
cs.LG2026
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…
stat.ML2025
Accelerated Distributional Temporal Difference Learning with Linear Function Approximation
Kaicheng Jin, Yang Peng, Jiansheng Yang +1
In this paper, we study the finite-sample statistical rates of distributional temporal difference (TD) learning with linear function approximation. The purpose of distributional TD…
cs.LG2025
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…