33 citations · 55 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…
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,…
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…