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20172026
most citedThe Computation of Approximate Generalized Feedback Nash Equilibria

34 citations · 123 across the 92 of their papers we have counts for

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math.OC2024

Sensing Resource Allocation Against Data-Poisoning Attacks in Traffic Routing

Yue Yu, Adam J. Thorpe, Jesse Milzman +2

Data-poisoning attacks can disrupt the efficient operations of transportation systems by misdirecting traffic flows via falsified data. One challenge in countering these attacks is…

math.OC2024

The computation of approximate feedback Stackelberg equilibria in multi-player nonlinear constrained dynamic games

Jingqi Li, Somayeh Sojoudi, Claire Tomlin +1

Solving feedback Stackelberg games with nonlinear dynamics and coupled constraints, a common scenario in practice, presents significant challenges. This work introduces an efficien…

math.OC2023

Risk-Minimizing Two-Player Zero-Sum Stochastic Differential Game via Path Integral Control

Apurva Patil, Yujing Zhou, David Fridovich-Keil +1

This paper addresses a continuous-time risk-minimizing two-player zero-sum stochastic differential game (SDG), in which each player aims to minimize its probability of failure. Fai…

math.OC2022★ 1 cited

Alternating Direction Method of Multipliers for Decomposable Saddle-Point Problems

Mustafa O. Karabag, David Fridovich-Keil, Ufuk Topcu

Saddle-point problems appear in various settings including machine learning, zero-sum stochastic games, and regression problems. We consider decomposable saddle-point problems and…

math.OC2021★ 34 cited

The Computation of Approximate Generalized Feedback Nash Equilibria

Forrest Laine, David Fridovich-Keil, Chih-Yuan Chiu +1

We present the concept of a Generalized Feedback Nash Equilibrium (GFNE) in dynamic games, extending the Feedback Nash Equilibrium concept to games in which players are subject to…

math.OC2019★ 18 cited

Feedback Linearization for Unknown Systems via Reinforcement Learning

Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar +4

We present a novel approach to control design for nonlinear systems which leverages model-free policy optimization techniques to learn a linearizing controller for a physical plant…