33 citations · 55 across the 13 of their papers we have counts for
13 papers
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization
Zhe Li, Bicheng Ying, Zidong Liu +2
Federated Learning (FL) offers a promising framework for collaborative and privacy-preserving machine learning across distributed data sources. However, the substantial communicati…
Towards Generalized Inverse Reinforcement Learning
Chaosheng Dong, Yijia Wang
This paper studies generalized inverse reinforcement learning (GIRL) in Markov decision processes (MDPs), that is, the problem of learning the basic components of an MDP given obse…
Federated Multi-Objective Learning
Haibo Yang, Zhuqing Liu, Jia Liu +2
In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications. However, existing algorithms i…
G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer
Yuchen Zhuang, Xin Shen, Yan Zhao +4
Sequential recommendation requires understanding the dynamic patterns of users' behaviors, contexts, and preferences from their historical interactions. Most existing works focus o…
AdaSelection: Accelerating Deep Learning Training through Data Subsampling
Minghe Zhang, Chaosheng Dong, Jinmiao Fu +8
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…
Multi-Label Learning to Rank through Multi-Objective Optimization
Debabrata Mahapatra, Chaosheng Dong, Yetian Chen +2
Learning to Rank (LTR) technique is ubiquitous in the Information Retrieval system nowadays, especially in the Search Ranking application. The query-item relevance labels typically…
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…
Inverse Multiobjective Optimization Through Online Learning
Chaosheng Dong, Yijia Wang, Bo Zeng
We study the problem of learning the objective functions or constraints of a multiobjective decision making model, based on a set of sequentially arrived decisions. In particular,…
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…