no code implementations • 24 Jan 2023 • Yuantong Li, Guang Cheng, Xiaowu Dai
In this paper, we propose a new algorithm for addressing the problem of matching markets with complementary preferences, where agents' preferences are unknown a priori and must be learned from data.
no code implementations • 23 Dec 2022 • Shuang Wu, Mingxuan Zhang, Yuantong Li, Carl Yang, Pan Li
On the other hand, due to the increasing demands for the protection of clients' data privacy, Federated Learning (FL) has been widely adopted: FL requires models to be trained in a multi-client system and restricts sharing of raw data among clients.
no code implementations • NAACL (GeBNLP) 2022 • Yuantong Li, Xiaokai Wei, Zijian Wang, Shen Wang, Parminder Bhatia, Xiaofei Ma, Andrew Arnold
People frequently interact with information retrieval (IR) systems, however, IR models exhibit biases and discrimination towards various demographics.
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 • 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 • 8 Aug 2021 • Pratik Ramprasad, Yuantong Li, Zhuoran Yang, Zhaoran Wang, Will Wei Sun, Guang Cheng
The recent emergence of reinforcement learning has created a demand for robust statistical inference methods for the parameter estimates computed using these algorithms.
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 • 19 Jun 2020 • Yuantong Li, Qi Ma, Sujit K. Ghosh
Estimating parameters of mixture model has wide applications ranging from classification problems to estimating of complex distributions.