4 citations · 6 across the 16 of their papers we have counts for
7 papers · 1 filter
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…