activity
20132024
most citedOn Gradient-Based Learning in Continuous Games

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

collaborators

66 papers

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,…

cs.LG2023★ 1 cited

Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows

Lauren Conger, Franca Hoffmann, Eric Mazumdar +1

We propose a novel framework for analyzing the dynamics of distribution shift in real-world systems that captures the feedback loop between learning algorithms and the distribution…

cs.GT2023

Consistent Conjectural Variations Equilibrium: Characterization & Stability for a Class of Continuous Games

Daniel J. Calderone, Benjamin J. Chasnov, Samuel A. Burden +1

Leveraging tools from the study of linear fractional transformations and algebraic Riccati equations, a local characterization of consistent conjectural variations equilibrium is g…

cs.LG2023

On the Limitations and Possibilities of Nash Regret Minimization in Zero-Sum Matrix Games under Noisy Feedback

Arnab Maiti, Kevin Jamieson, Lillian J. Ratliff

This paper studies a variant of two-player zero-sum matrix games, where, at each timestep, the row player selects row , the column player selects column , and the row player…

cs.AI2023

Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula

Boling Yang, Liyuan Zheng, Lillian J. Ratliff +2

Autocurricular training is an important sub-area of multi-agent reinforcement learning~(MARL) that allows multiple agents to learn emergent skills in an unsupervised co-evolving sc…

cs.AI2023★ 2 cited

Human adaptation to adaptive machines converges to game-theoretic equilibria

Benjamin J. Chasnov, Lillian J. Ratliff, Samuel A. Burden

Adaptive machines have the potential to assist or interfere with human behavior in a range of contexts, from cognitive decision-making to physical device assistance. Therefore it i…