no code implementations • 19 Nov 2023 • Yihan Zhang, My T. Thai, Jie Wu, Hongchang Gao
To the best of our knowledge, this is the first stochastic algorithm achieving these theoretical results under the heterogeneous setting.
no code implementations • 28 Aug 2023 • Yihan Zhang, Hong Chang Ji, Ramji Venkataramanan, Marco Mondelli
Our methodology is general, and opens the way to the precise characterization of spiked matrices and of the corresponding spectral methods in a variety of settings.
no code implementations • 29 Apr 2023 • Chuqin Geng, Yihan Zhang, Brigitte Pientka, Xujie Si
The recent introduction of ChatGPT has drawn significant attention from both industry and academia due to its impressive capabilities in solving a diverse range of tasks, including language translation, text summarization, and computer programming.
no code implementations • 24 Apr 2023 • Yihan Zhang, Wenhao Jiang, Feng Zheng, Chiu C. Tan, Xinghua Shi, Hongchang Gao
This motivates us to study decentralized minimax optimization algorithms for the nonconvex-nonconcave problem.
1 code implementation • 13 Apr 2023 • Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang, Winnie Chow, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, Tong Zhang
Utilizing a reward model and a sufficient number of samples, our approach selects the high-quality samples, discarding those that exhibit undesired behavior, and subsequently enhancing the model by fine-tuning on these filtered samples.
no code implementations • 21 Nov 2022 • Yihan Zhang, Marco Mondelli, Ramji Venkataramanan
In a mixed generalized linear model, the objective is to learn multiple signals from unlabeled observations: each sample comes from exactly one signal, but it is not known which one.
no code implementations • 2 Jul 2022 • Xin Tong, Zhaoyang Zhang, Yihan Zhang, Zhaohui Yang, Chongwen Huang, Kai-Kit Wong, Merouane Debbah
In this paper, we consider the problem of sensing the environment within a wireless cellular framework.
no code implementations • 6 Jun 2022 • Yihan Zhang, Nir Weinberger
In this model, an estimator observes $n$ samples of a $d$-dimensional parameter vector $\theta_{*}\in\mathbb{R}^{d}$, multiplied by a random sign $ S_i $ ($1\le i\le n$), and corrupted by isotropic standard Gaussian noise.
no code implementations • 29 Jan 2021 • Yihan Zhang
In particular, the treatment of marginal confusability does not follow from the point-to-point results by Wang et al. Our achievability results follow from random coding with expurgation.
Information Theory Information Theory
no code implementations • 17 Nov 2020 • Tao Huang, Yihan Zhang, Jiajing Wu, Junyuan Fang, Zibin Zheng
To tackle the dilemma between accuracy and efficiency, we propose to use aggregators with different granularities to gather neighborhood information in different layers.