2 citations · 3 across the 2 of their papers we have counts for
6 papers
Incentivized Bandit Learning with Self-Reinforcing User Preferences
Tianchen Zhou, Jia Liu, Chaosheng Dong +1
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 pu…
One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning
Chaosheng Dong, Xiaojie Jin, Weihao Gao +5
Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments. The sheer volume of streaming training…
Learning Risk Preferences from Investment Portfolios Using Inverse Optimization
Shi Yu, Haoran Wang, Chaosheng Dong
The fundamental principle in Modern Portfolio Theory (MPT) is based on the quantification of the portfolio's risk related to performance. Although MPT has made huge impacts on the…
Wasserstein Distributionally Robust Inverse Multiobjective Optimization
Chaosheng Dong, Bo Zeng
Inverse multiobjective optimization provides a general framework for the unsupervised learning task of inferring parameters of a multiobjective decision making problem (DMP), based…
Generalized Inverse Optimization through Online Learning
Chaosheng Dong, Yiran Chen, Bo Zeng
Inverse optimization is a powerful paradigm for learning preferences and restrictions that explain the behavior of a decision maker, based on a set of external signal and the corre…
Inferring Parameters Through Inverse Multiobjective Optimization
Chaosheng Dong, Bo Zeng
Given a set of human's decisions that are observed, inverse optimization has been developed and utilized to infer the underlying decision making problem. The majority of existing s…