no code implementations • 8 Nov 2023 • Tianchen Zhou, Jia Liu, Yang Jiao, Chaosheng Dong, Yetian Chen, Yan Gao, Yi Sun
Online learning to rank (ONL2R) is a foundational problem for recommender systems and has received increasing attention in recent years.
no code implementations • 19 Jun 2023 • Minghe Zhang, Chaosheng Dong, Jinmiao Fu, Tianchen Zhou, Jia Liang, Jia Liu, Bo Liu, Michinari Momma, Bryan Wang, Yan Gao, Yi Sun
In this paper, we introduce AdaSelection, an adaptive sub-sampling method to identify the most informative sub-samples within each minibatch to speed up the training of large-scale deep learning models without sacrificing model performance.
1 code implementation • 4 Apr 2023 • Tianchen Zhou, Zhanyi Hu, Bingzhe Wu, Cen Chen
Data privacy concerns has made centralized training of data, which is scattered across silos, infeasible, leading to the need for collaborative learning frameworks.
no code implementations • ICLR 2022 • Tianchen Zhou, Jia Liu, Chaosheng Dong, Yi Sun
We show that the delay impacts in both cases can still be upper bounded by an additive penalty on both the regret and total incentive costs.
no code implementations • 19 May 2021 • Tianchen Zhou, Jia Liu, Chaosheng Dong, Jingyuan Deng
In this paper, we investigate a new multi-armed bandit (MAB) online learning model that considers real-world phenomena in many recommender systems: (i) the learning agent cannot pull the arms by itself and thus has to offer rewards to users to incentivize arm-pulling indirectly; and (ii) if users with specific arm preferences are well rewarded, they induce a "self-reinforcing" effect in the sense that they will attract more users of similar arm preferences.