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}).
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 • 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 • 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.
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 • 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.
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 • 7 May 2022 • Yuantong Li, Chi-Hua Wang, Guang Cheng, Will Wei Sun
Existing works focus on multi-armed bandit with static preference, but this is insufficient: the two-sided preference changes as along as one-side's contextual information updates, resulting in non-static matching.
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 • 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 • 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 • 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 • 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.