no code implementations • 24 May 2024 • Lan Tao, SHIRONG XU, Chi-Hua Wang, Namjoon Suh, Guang Cheng
In particular, this paper establishes theoretical results regarding the convergence rate of the estimation error of TV distance between two Gaussian distributions.
no code implementations • 24 May 2024 • Chi-Hua Wang, Guang Cheng
We present BadGD, a unified theoretical framework that exposes the vulnerabilities of gradient descent algorithms through strategic backdoor attacks.
no code implementations • 1 Jan 2024 • Din-Yin Hsieh, Chi-Hua Wang, Guang Cheng
Exploring generative model training for synthetic tabular data, specifically in sequential contexts such as credit card transaction data, presents significant challenges.
no code implementations • 1 Jan 2024 • Yinan Cheng, Chi-Hua Wang, Vamsi K. Potluru, Tucker Balch, Guang Cheng
Devising procedures for downstream task-oriented generative model selections is an unresolved problem of practical importance.
no code implementations • 28 Nov 2022 • Yucong Liu, Chi-Hua Wang, Guang Cheng
Devising procedures for auditing generative model privacy-utility tradeoff is an important yet unresolved problem in practice.
no code implementations • 18 Nov 2022 • Chi-Hua Wang, Wenjie Li
Always-valid concentration inequalities are increasingly used as performance measures for online statistical learning, notably in the learning of generative models and supervised learning.
no code implementations • 19 Aug 2022 • Po-Yi Liu, Chi-Hua Wang, Henghsiu Tsai
This paper presents a novel non-stationary dynamic pricing algorithm design, where pricing agents face incomplete demand information and market environment shifts.
no code implementations • 7 May 2022 • Yuantong Li, Chi-Hua Wang, Guang Cheng, Will Wei Sun
The key component of the proposed dynamic matching algorithm is an online estimation of the preference ranking with a statistical guarantee.
no code implementations • 27 Feb 2022 • Chi-Hua Wang, Wenjie Li, Guang Cheng, Guang Lin
This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits with high-dimensional decision context and coupled through common global parameters.
no code implementations • 23 Feb 2022 • Shuang Wu, Chi-Hua Wang, Yuantong Li, Guang Cheng
We propose a new bootstrap-based online algorithm for stochastic linear bandit problems.
1 code implementation • 17 Jun 2021 • Wenjie Li, Chi-Hua Wang, Guang Cheng, Qifan Song
In this paper, we make the key delineation on the roles of resolution and statistical uncertainty in hierarchical bandits-based black-box optimization algorithms, guiding a more general analysis and a more efficient algorithm design.
no code implementations • 3 Dec 2020 • Yuantong Li, Chi-Hua Wang, Guang Cheng
Motivated by the EU's "Right To Be Forgotten" regulation, we initiate a study of statistical data deletion problems where users' data are accessible only for a limited period of time.
no code implementations • 5 Jul 2020 • Chi-Hua Wang, Zhanyu Wang, Will Wei Sun, Guang Cheng
In this paper, we propose a novel approach for designing dynamic pricing policy based regularized online statistical learning with theoretical guarantees.
no code implementations • 21 Feb 2020 • Chi-Hua Wang, Guang Cheng
In such a scenario, our goal is to allocate a batch of treatments to maximize treatment efficacy based on observed high-dimensional user covariates.
no code implementations • 19 Feb 2020 • Chi-Hua Wang, Yang Yu, Botao Hao, Guang Cheng
In this paper, we propose a novel perturbation-based exploration method in bandit algorithms with bounded or unbounded rewards, called residual bootstrap exploration (\texttt{ReBoot}).