K
Kaixiang Lin
Researcher at Michigan State University
Publications - 31
Citations - 1154
Kaixiang Lin is an academic researcher from Michigan State University. The author has contributed to research in topics: Reinforcement learning & Computer science. The author has an hindex of 10, co-authored 24 publications receiving 628 citations. Previous affiliations of Kaixiang Lin include University of Science and Technology of China.
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Differentially Private Generative Adversarial Network.
TL;DR: This paper proposes a differentially private GAN (DPGAN) model, in which it is demonstrated that the method can generate high quality data points at a reasonable privacy level by adding carefully designed noise to gradients during the learning procedure.
Proceedings ArticleDOI
Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning
TL;DR: In this paper, a contextual multi-agent reinforcement learning framework was proposed to achieve explicit coordination among a large number of agents adaptive to different contexts in large-scale fleet management problem.
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Transfer Learning in Deep Reinforcement Learning: A Survey
TL;DR: This survey surveys the field of transfer learning in the problem setting of Reinforcement Learning, providing a systematic categorization of its state-of-the-art techniques.
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Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning.
TL;DR: In this article, a contextual multi-agent reinforcement learning framework was proposed to achieve explicit coordination among a large number of agents adaptive to different contexts in large-scale fleet management problem.
Proceedings ArticleDOI
Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates
TL;DR: A privacy-preserving proximal gradient algorithm, which asynchronously updates models of the learning tasks, is introduced to solve a general class of MTL formulations.