no code implementations • 21 Oct 2023 • Chao Wang, Caixing Wang, Xin He, Xingdong Feng
This paper focuses on investigating the transfer learning problem within the context of nonparametric regression over a reproducing kernel Hilbert space.
1 code implementation • NeurIPS 2023 • Xingdong Feng, Xin He, Caixing Wang, Chao Wang, Jingnan Zhang
Two types of covariate shift problems are the focus of this paper and the sharp convergence rates are established for a general loss function to provide a unified theoretical analysis, which concurs with the optimal results in literature where the squared loss is used.
no code implementations • 6 Oct 2021 • Xingdong Feng, Yuan Gao, Jian Huang, Yuling Jiao, Xu Liu
We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions.
no code implementations • NeurIPS 2020 • Fan Zhou, Jianing Wang, Xingdong Feng
Distributional reinforcement learning (DRL) estimates the distribution over future returns instead of the mean to more efficiently capture the intrinsic uncertainty of MDPs.
no code implementations • 29 Apr 2020 • Sanyou Wu, Xingdong Feng, Fan Zhou
Deep semi-supervised learning has been widely implemented in the real-world due to the rapid development of deep learning.
no code implementations • 3 Jan 2019 • Xin He, Yeheng Ge, Xingdong Feng
In statistical learning, identifying underlying structures of true target functions based on observed data plays a crucial role to facilitate subsequent modeling and analysis.