activity
20132024
most citedOn Gradient-Based Learning in Continuous Games

78 citations · 333 across the 57 of their papers we have counts for

collaborators
Showing eess.SYShow all

5 papers · 1 filter

eess.SY2024

Distribution-Free Guarantees for Systems with Decision-Dependent Noise

Heling Zhang, Lillian J. Ratliff, Roy Dong

In many real-world dynamical systems, obtaining precise models of system uncertainty remains a challenge. It may be difficult to estimate noise distributions or robustness bounds,…

eess.SY2019★ 26 cited

Global Convergence of Policy Gradient for Sequential Zero-Sum Linear Quadratic Dynamic Games

Jingjing Bu, Lillian J. Ratliff, Mehran Mesbahi

We propose projection-free sequential algorithms for linear-quadratic dynamics games. These policy gradient based algorithms are akin to Stackelberg leadership model and can be ext…

eess.SY2016★ 4 cited

To Observe or Not to Observe: Queuing Game Framework for Urban Parking

Lillian J. Ratliff, Chase Dowling, Eric Mazumdar +1

We model parking in urban centers as a set of parallel queues and overlay a game theoretic structure that allows us to compare the user-selected (Nash) equilibrium to the socially…

eess.SY2013

Blind Identification via Lifting

Henrik Ohlsson, Lillian J. Ratliff, Roy Dong +1

Blind system identification is known to be an ill-posed problem and without further assumptions, no unique solution is at hand. In this contribution, we are concerned with the task…

eess.SY2013★ 7 cited

Blind Identification of ARX Models with Piecewise Constant Inputs

Henrik Ohlsson, Lillian Ratliff, Roy Dong +1

Blind system identification is known to be a hard ill-posed problem and without further assumptions, no unique solution is at hand. In this contribution, we are concerned with the…