Search Results for author: Lillian Ratliff

Found 10 papers, 4 papers with code

Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study

no code implementations ICML 2020 Tanner Fiez, Benjamin Chasnov, Lillian Ratliff

Contemporary work on learning in continuous games has commonly overlooked the hierarchical decision-making structure present in machine learning problems formulated as games, instead treating them as simultaneous play games and adopting the Nash equilibrium solution concept.

Decision Making

Instance-optimal PAC Algorithms for Contextual Bandits

no code implementations5 Jul 2022 Zhaoqi Li, Lillian Ratliff, Houssam Nassif, Kevin Jamieson, Lalit Jain

In the stochastic contextual bandit setting, regret-minimizing algorithms have been extensively researched, but their instance-minimizing best-arm identification counterparts remain seldom studied.

Multi-Armed Bandits

Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games

no code implementations NeurIPS 2021 Tanner Fiez, Lillian Ratliff, Eric Mazumdar, Evan Faulkner, Adhyyan Narang

For the class of nonconvex-PL zero-sum games, we exploit timescale separation to construct a potential function that when combined with the stability characterization and an asymptotic saddle avoidance result gives a global asymptotic almost-sure convergence guarantee to a set of the strict local minmax equilibrium.

Online Learning in Periodic Zero-Sum Games

no code implementations NeurIPS 2021 Tanner Fiez, Ryann Sim, Stratis Skoulakis, Georgios Piliouras, Lillian Ratliff

Classical learning results build on this theorem to show that online no-regret dynamics converge to an equilibrium in a time-average sense in zero-sum games.

online learning

Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games

1 code implementation15 Dec 2020 Stratis Skoulakis, Tanner Fiez, Ryann Sim, Georgios Piliouras, Lillian Ratliff

The predominant paradigm in evolutionary game theory and more generally online learning in games is based on a clear distinction between a population of dynamic agents that interact given a fixed, static game.

online learning

Gradient Descent-Ascent Provably Converges to Strict Local Minmax Equilibria with a Finite Timescale Separation

1 code implementation ICLR 2021 Tanner Fiez, Lillian Ratliff

In this work, we bridge the gap between past work by showing there exists a finite timescale separation parameter $\tau^{\ast}$ such that $x^{\ast}$ is a stable critical point of gradient descent-ascent for all $\tau \in (\tau^{\ast}, \infty)$ if and only if it is a strict local minmax equilibrium.

A SUPER* Algorithm to Optimize Paper Bidding in Peer Review

1 code implementation27 Jun 2020 Tanner Fiez, Nihar B. Shah, Lillian Ratliff

Theoretically, we show a local optimality guarantee of our algorithm and prove that popular baselines are considerably suboptimal.

Constrained Upper Confidence Reinforcement Learning with Known Dynamics

no code implementations L4DC 2020 Liyuan Zheng, Lillian Ratliff

Constrained Markov Decision Processes are a class of stochastic decision problems in which the decision maker must select a policy that satisfies auxiliary cost constraints.

reinforcement-learning

Sequential Experimental Design for Transductive Linear Bandits

1 code implementation NeurIPS 2019 Tanner Fiez, Lalit Jain, Kevin Jamieson, Lillian Ratliff

Such a transductive setting naturally arises when the set of measurement vectors is limited due to factors such as availability or cost.

Drug Discovery Experimental Design +1

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