76 citations · 308 across the 72 of their papers we have counts for
21 papers · 1 filter
Wasserstein Distributionally Robust Nash Equilibrium Seeking with Heterogeneous Data: A Lagrangian Approach
Zifan Wang, Georgios Pantazis, Sergio Grammatico +2
We study a class of distributionally robust games where agents are allowed to heterogeneously choose their risk aversion with respect to distributional shifts of the uncertainty. I…
First- and Zeroth-Order Learning in Asynchronous Games
Zifan Wang, Xinlei Yi, Michael M. Zavlanos +1
This paper investigates the discrete-time asynchronous games in which noncooperative agents seek to minimize their individual cost functions. Building on the assumption of partial…
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…
Learning of Nash Equilibria in Risk-Averse Games
Zifan Wang, Yi Shen, Michael M. Zavlanos +1
This paper considers risk-averse learning in convex games involving multiple agents that aim to minimize their individual risk of incurring significantly high costs. Specifically,…
Policy Evaluation in Distributional LQR (Extended Version)
Zifan Wang, Yulong Gao, Siyi Wang +3
Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random retu…
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…
Convergence Analysis of the Best Response Algorithm for Time-Varying Games
Zifan Wang, Yi Shen, Michael M. Zavlanos +1
This paper studies a class of strongly monotone games involving non-cooperative agents that optimize their own time-varying cost functions. We assume that the agents can observe ot…
Asymmetric Feedback Learning in Online Convex Games
Zifan Wang, Xinlei Yi, Yi Shen +2
This paper considers convex games involving multiple agents that aim to minimize their own cost functions using locally available information. A common assumption in the study of s…
Policy Evaluation in Distributional LQR
Zifan Wang, Yulong Gao, Siyi Wang +3
Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random retu…
Asynchronous Zeroth-Order Distributed Optimization with Residual Feedback
Yi Shen, Yan Zhang, Scott Nivison +2
We consider a zeroth-order distributed optimization problem, where the global objective function is a black-box function and, as such, its gradient information is inaccessible to t…
A New One-Point Residual-Feedback Oracle For Black-Box Learning and Control
Yan Zhang, Yi Zhou, Kaiyi Ji +1
Zeroth-order optimization (ZO) algorithms have been recently used to solve black-box or simulation-based learning and control problems, where the gradient of the objective function…
A Distributed Online Convex Optimization Algorithm with Improved Dynamic Regret
Yan Zhang, Robert J. Ravier, Michael M. Zavlanos +1
In this paper, we consider the problem of distributed online convex optimization, where a network of local agents aim to jointly optimize a convex function over a period of multipl…
Distributed Online Convex Optimization with Improved Dynamic Regret
Yan Zhang, Robert J. Ravier, Vahid Tarokh +1
In this paper, we consider the problem of distributed online convex optimization, where a group of agents collaborate to track the global minimizers of a sum of time-varying object…
Gradient-Free Multi-Agent Nonconvex Nonsmooth Optimization
Davood Hajinezhad, Michael Zavlanos
In this paper, we consider the problem of minimizing the sum of nonconvex and possibly nonsmooth functions over a connected multi-agent network, where the agents have partial knowl…
Distributed Constrained Online Learning
Santiago Paternain, Soomin Lee, Michael M. Zavlanos +1
In this paper, we consider groups of agents in a network that select actions in order to satisfy a set of constraints that vary arbitrarily over time and minimize a time-varying fu…
An Optimal Graph-Search Method for Secure State Estimation
Xusheng Luo, Miroslav Pajic, Michael M. Zavlanos
The growing complexity of modern Cyber-Physical Systems (CPS) and the frequent communication between their components make them vulnerable to malicious attacks. As a result, secure…
Augmented Lagrangian Optimization under Fixed-Point Arithmetic
Yan Zhang, Michael M. Zavlanos
In this paper, we propose an inexact Augmented Lagrangian Method (ALM) for the optimization of convex and nonsmooth objective functions subject to linear equality constraints and b…
On the Sublinear Regret of Distributed Primal-Dual Algorithms for Online Constrained Optimization
Soomin Lee, Michael M. Zavlanos
This paper introduces consensus-based primal-dual methods for distributed online optimization where the time-varying system objective function is given as the sum…
Complexity Certification of a Distributed Augmented Lagrangian Method
Soomin Lee, Nikolaos Chatzipanagiotis, Michael M. Zavlanos
In this paper we present complexity certification results for a distributed Augmented Lagrangian (AL) algorithm used to solve convex optimization problems involving globally couple…
Approximate Projection Methods for Decentralized Optimization with Functional Constraints
Soomin Lee, Michael M. Zavlanos
We consider distributed convex optimization problems that involve a separable objective function and nontrivial functional constraints, such as Linear Matrix Inequalities (LMIs). W…
Spectral Design of Dynamic Networks via Local Operations
Victor M. Preciado, Michael M. Zavlanos, Ali Jadbabaie
Motivated by the relationship between the eigenvalue spectrum of the Laplacian matrix of a network and the behavior of dynamical processes evolving in it, we propose a distributed…