4 citations · 6 across the 16 of their papers we have counts for
16 papers
A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing
Xiaoxing Ren, Thomas Parisini, Andreas A. Malikopoulos
We study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models. The proposed framework combines team-theoretic equivalen…
Communication-Efficient Distributed Learning with Differential Privacy
Xiaoxing Ren, Yuwen Ma, Nicola Bastianello +3
We address nonconvex learning problems over undirected networks. In particular, we focus on the challenge of designing an algorithm that is both communication-efficient and that gu…
A Functional Learning Approach for Team-Optimal Traffic Coordination
Weihao Sun, Gehui Xu, Alessio Moreschini +2
In this paper, we develop a kernel-based policy iteration functional learning framework for computing team-optimal strategies in traffic coordination problems. We consider a multi-…
Adaptive Mitigation of Insider Threats via Off-Policy Learning
Gehui Xu, Kaiwen Chen, Zhong-Ping Jiang +2
An insider is a team member who covertly deviates from the team's optimal collaborative strategy to pursue a private objective while still appearing cooperative. Such an insider ma…
Game-Theoretic Learning-Based Mitigation of Insider Threats
Gehui Xu, Kaiwen Chen, Thomas Parisini +1
An insider is defined as a team member who covertly deviates from the team's optimal collaborative control strategy in pursuit of a private objective, while maintaining an outward…
A Communication-Efficient Decentralized Actor-Critic Algorithm
Xiaoxing Ren, Nicola Bastianello, Thomas Parisini +1
In this paper, we study the problem of reinforcement learning in multi-agent systems where communication among agents is limited. We develop a decentralized actor-critic learning f…
When Does Selfishness Align with Team Goals? A Structural Analysis of Equilibrium and Optimality
Gehui Xu, Thomas Parisini, Andreas A. Malikopoulos
This paper investigates the relationship between the team-optimal solution and the Nash equilibrium (NE) to assess the impact of self-interested decisions on team performance. In c…
Projected Gradient Descent for Constrained Decision-Dependent Optimization
Zifan Wang, Changxin Liu, Thomas Parisini +2
This paper considers the decision-dependent optimization problem, where the data distributions react in response to decisions affecting both the objective function and linear const…
Jointly Computation- and Communication-Efficient Distributed Learning
Xiaoxing Ren, Nicola Bastianello, Karl H. Johansson +1
We address distributed learning problems over undirected networks. Specifically, we focus on designing a novel ADMM-based algorithm that is jointly computation- and communication-e…
Deviation Between Team-Optimal Solution and Nash Equilibrium in Flow Assignment Problems
Gehui Xu, Ting Bai, Andreas A. Malikopoulos +1
We investigate the relationship between the team-optimal solution and the Nash equilibrium (NE) to assess the impact of strategy deviation on team performance. As a working use cas…
Communication-Efficient Stochastic Distributed Learning
Xiaoxing Ren, Nicola Bastianello, Karl H. Johansson +1
We address distributed learning problems, both nonconvex and convex, over undirected networks. In particular, we design a novel algorithm based on the distributed Alternating Direc…
A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements
Gehui Xu, Guanpu Chen, Baris Fidan +4
Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. W…
Inverse learning of black-box aggregator for robust Nash equilibrium
Guanpu Chen, Gehui Xu, Fengxiang He +3
In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an…
Global solution to sensor network localization: A non-convex potential game approach and its distributed implementation
Gehui Xu, Guanpu Chen, Yiguang Hong +3
Consider a sensor network consisting of both anchor and non-anchor nodes. We address the following sensor network localization (SNL) problem: given the physical locations of anchor…
Non-convex potential games for finding global solutions to sensor network localization
Gehui Xu, Guanpu Chen, Yiguang Hong +3
Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and th…
Constrained Optimization with Decision-Dependent Distributions
Zifan Wang, Changxin Liu, Thomas Parisini +2
In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization prob…