Tanner Fiez | DeepAI

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Featured Co-authors

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Shreyas Sekar
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∙ 10/25/2022

Adaptive Experimental Design and Counterfactual Inference

Adaptive experimental design methods are increasingly being used in indu...

0 Tanner Fiez, et al. ∙

∙ 11/05/2021

Online Learning in Periodic Zero-Sum Games

A seminal result in game theory is von Neumann's minmax theorem, which s...

0 Tanner Fiez, et al. ∙

∙ 09/25/2021

Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms

The hierarchical interaction between the actor and critic in actor-criti...

0 Liyuan Zheng, et al. ∙

∙ 06/02/2021

Minimax Optimization with Smooth Algorithmic Adversaries

This paper considers minimax optimization min_x max_y f(x, y) in the cha...

23 Tanner Fiez, et al. ∙

∙ 12/15/2020

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

The predominant paradigm in evolutionary game theory and more generally ...

0 Stratis Skoulakis, et al. ∙

∙ 09/30/2020

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

We study the role that a finite timescale separation parameter τ has on ...

0 Tanner Fiez, et al. ∙

∙ 06/27/2020

A SUPER* Algorithm to Optimize Paper Bidding in Peer Review

A number of applications involve sequential arrival of users, and requir...

0 Tanner Fiez, et al. ∙

∙ 06/20/2019

Sequential Experimental Design for Transductive Linear Bandits

In this paper we introduce the transductive linear bandit problem: given...

0 Tanner Fiez, et al. ∙

∙ 06/04/2019

Convergence of Learning Dynamics in Stackelberg Games

This paper investigates the convergence of learning dynamics in Stackelb...

0 Tanner Fiez, et al. ∙

∙ 07/06/2018

Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences

The design of personalized incentives or recommendations to improve user...

0 Tanner Fiez, et al. ∙

∙ 06/14/2018

Adaptive Incentive Design

We apply control theoretic and optimization techniques to adaptively des...

0 Lillian J. Ratliff, et al. ∙

∙ 03/11/2018

Incentives in the Dark: Multi-armed Bandits for Evolving Users with Unknown Type

Design of incentives or recommendations to users is becoming more common...

0 Lillian J. Ratliff, et al. ∙

∙ 12/02/2017

Data-Driven Spatio-Temporal Analysis of Curbside Parking Demand: A Case-Study in Seattle

Due to rapid expansion of urban areas in recent years, management of cur...

0 Tanner Fiez, et al. ∙

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