1 citations · 3 across the 15 of their papers we have counts for
16 papers
Learning Multi-Timescale Interventions under Safety and Resource Constraints
David Mguni, Wanrong Yang, Jing Dong +5
Many sequential decision problems offer qualitatively different ways of influencing the environment: some interventions act immediately, whereas others induce persistent effects th…
Pointwise Convergence in Games with Conflicting Interest
Nanxiang Zhou, Jing Dong, Baoxiang Wang
In this work, we introduce the concept of non-negative weighted regret, an extension of non-negative regret \cite{anagnostides2022last} in games. Investigating games with non-negat…
On the Decomposition of Differential Game
Nanxiang Zhou, Jing Dong, Yutian Li +1
To understand the complexity of the dynamic of learning in differential games, we decompose the game into components where the dynamic is well understood. One of the possible tools…
Last-iterate Convergence in Regularized Graphon Mean Field Game
Jing Dong, Baoxiang Wang, Yaoliang Yu
To model complex real-world systems, such as traders in stock markets, or the dissemination of contagious diseases, graphon mean-field games (GMFG) have been proposed to model many…
Uncoupled and Convergent Learning in Monotone Games under Bandit Feedback
Jing Dong, Baoxiang Wang, Yaoliang Yu
We study the problem of no-regret learning algorithms for general monotone and smooth games and their last-iterate convergence properties. Specifically, we investigate the problem…
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games
Jing Dong, Baoxiang Wang, Yaoliang Yu
In this work, we study potential games and Markov potential games under stochastic cost and bandit feedback. We propose a variant of the Frank-Wolfe algorithm with sufficient explo…
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning
Canzhe Zhao, Yanjie Ze, Jing Dong +2
Communication lays the foundation for cooperation in human society and in multi-agent reinforcement learning (MARL). Humans also desire to maintain their privacy when communicating…
Taming the Exponential Action Set: Sublinear Regret and Fast Convergence to Nash Equilibrium in Online Congestion Games
Jing Dong, Jingyu Wu, Siwei Wang +2
The congestion game is a powerful model that encompasses a range of engineering systems such as traffic networks and resource allocation. It describes the behavior of a group of ag…
A Batch-to-Online Transformation under Random-Order Model
Jing Dong, Yuichi Yoshida
We introduce a transformation framework that can be utilized to develop online algorithms with low -approximate regret in the random-order model from offline approximation algor…
Online Control with Adversarial Disturbance for Continuous-time Linear Systems
Jingwei Li, Jing Dong, Can Chang +2
We study online control for continuous-time linear systems with finite sampling rates, where the objective is to design an online procedure that learns under non-stochastic noise a…
Online Policy Optimization for Robust MDP
Jing Dong, Jingwei Li, Baoxiang Wang +1
Reinforcement learning (RL) has exceeded human performance in many synthetic settings such as video games and Go. However, real-world deployment of end-to-end RL models is less com…
Algorithms and Theory for Supervised Gradual Domain Adaptation
Jing Dong, Shiji Zhou, Baoxiang Wang +1
The phenomenon of data distribution evolving over time has been observed in a range of applications, calling the needs of adaptive learning algorithms. We thus study the problem of…
Provably Efficient Convergence of Primal-Dual Actor-Critic with Nonlinear Function Approximation
Jing Dong, Li Shen, Yinggan Xu +1
We study the convergence of the actor-critic algorithm with nonlinear function approximation under a nonconvex-nonconcave primal-dual formulation. Stochastic gradient descent ascen…
Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization
Canzhe Zhao, Yanjie Ze, Jing Dong +2
Temporal difference (TD) learning is a widely used method to evaluate policies in reinforcement learning. While many TD learning methods have been developed in recent years, little…
Cascading Bandit under Differential Privacy
Kun Wang, Jing Dong, Baoxiang Wang +2
This paper studies \emph{differential privacy (DP)} and \emph{local differential privacy (LDP)} in cascading bandits. Under DP, we propose an algorithm which guarantees -indisti…
Combinatorial Bandits under Strategic Manipulations
Jing Dong, Ke Li, Shuai Li +1
Strategic behavior against sequential learning methods, such as "click framing" in real recommendation systems, have been widely observed. Motivated by such behavior we study the p…