no code implementations • 3 Dec 2024 • Pengjie Zhou, Haoyu Wei, Huiming Zhang
Reinforcement Learning (RL) is a widely researched area in artificial intelligence that focuses on teaching agents decision-making through interactions with their environment.
no code implementations • 25 Dec 2023 • Haoyu Wei, Runzhe Wan, Lei Shi, Rui Song
Many real-world bandit applications are characterized by sparse rewards, which can significantly hinder learning efficiency.
no code implementations • 13 Mar 2023 • Huiming Zhang, Haoyu Wei, Guang Cheng
In non-asymptotic learning, variance-type parameters of sub-Gaussian distributions are of paramount importance.
no code implementations • 3 Feb 2023 • Runzhe Wan, Haoyu Wei, Branislav Kveton, Rui Song
Despite the great interest in the bandit problem, designing efficient algorithms for complex models remains challenging, as there is typically no analytical way to quantify uncertainty.
no code implementations • 11 Feb 2022 • Haoyu Wei, Xiaoyu Lei, Yixin Han, Huiming Zhang
Identifying important features linked to a response variable is a fundamental task in various scientific domains.
no code implementations • 2 Nov 2021 • Jing Luo, Haoyu Wei, Xiaoyu Lei, Jiaxin Guo
For the differential privacy under the sub-Gamma noise, we derive the asymptotic properties of a class of network models with binary values with a general link function.
no code implementations • 10 Oct 2021 • Haoyu Wei, Xiaojun Song
In this article, we propose an easy-to-use method to testing the normality assumption in ANOVA models by using smooth tests.
no code implementations • 10 Oct 2021 • Xiaojun Song, Haoyu Wei
We propose consistent nonparametric tests of conditional independence for time series data.
no code implementations • 7 Oct 2021 • Shaomin Li, Haoyu Wei, Xiaoyu Lei
This paper studies the non-asymptotic merits of the double $\ell_1$-regularized for heterogeneous overdispersed count data via negative binomial regressions.
no code implementations • 4 Feb 2021 • Huiming Zhang, Haoyu Wei
Constant-specified and exponential concentration inequalities play an essential role in the finite-sample theory of machine learning and high-dimensional statistics area.
1 code implementation • 8 Dec 2020 • Haoyu Wei, Florian Schiffers, Tobias Würfl, Daming Shen, Daniel Kim, Aggelos K. Katsaggelos, Oliver Cossairt
Computed tomography is widely used to examine internal structures in a non-destructive manner.
no code implementations • 14 Nov 2019 • Xiaowei Yang, Huiming Zhang, Haoyu Wei, Shouzheng Zhang
It shows that our method has potency and superiority of detecting the shape of multi-mode density compared with other conventional approaches.