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06/22/2023
Logarithmic Regret for Matrix Games against an Adversary with Noisy Bandit Feedback
This paper considers a variant of zero-sum matrix games where at each ti...
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05/19/2023
Consistent Conjectural Variations Equilibrium: Characterization Stability for a Class of Continuous Games
Leveraging tools from the study of linear fractional transformations and...
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05/04/2023
Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula
Autocurricular training is an important sub-area of multi-agent reinforc...
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05/01/2023
Human adaptation to adaptive machines converges to game-theoretic equilibria
Adaptive machines have the potential to assist or interfere with human b...
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03/19/2023
Instance-dependent Sample Complexity Bounds for Zero-sum Matrix Games
We study the sample complexity of identifying an approximate equilibrium...
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07/07/2022
Interactive Combinatorial Bandits: Balancing Competitivity and Complementarity
We study non-modular function maximization in the online interactive ban...
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06/06/2022
Multi-learner risk reduction under endogenous participation dynamics
Prediction systems face exogenous and endogenous distribution shift – th...
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05/05/2022
General sum stochastic games with networked information flows
Inspired by applications such as supply chain management, epidemics, and...
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04/08/2022
Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments
This paper studies the problem of expected loss minimization given a dat...
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01/10/2022
Multiplayer Performative Prediction: Learning in Decision-Dependent Games
Learning problems commonly exhibit an interesting feedback mechanism whe...
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11/18/2021
Improved rates for derivative free play in convex games
The influential work of Bravo et al. 2018 shows that derivative free pla...
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09/25/2021
Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms
The hierarchical interaction between the actor and critic in actor-criti...
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06/30/2021
Which Echo Chamber? Regions of Attraction in Learning with Decision-Dependent Distributions
As data-driven methods are deployed in real-world settings, the processe...
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06/16/2021
Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization
Min-max optimization is emerging as a key framework for analyzing proble...
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06/02/2021
Minimax Optimization with Smooth Algorithmic Adversaries
This paper considers minimax optimization min_x max_y f(x, y) in the cha...
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12/23/2020
Function Design for Improved Competitive Ratio in Online Resource Allocation with Procurement Costs
We study the problem of online resource allocation, where multiple custo...
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11/07/2020
Stability of Gradient Learning Dynamics in Continuous Games: Vector Action Spaces
Towards characterizing the optimization landscape of games, this paper a...
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11/07/2020
Stability of Gradient Learning Dynamics in Continuous Games: Scalar Action Spaces
Learning processes in games explain how players grapple with one another...
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03/20/2020
Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks
This paper focuses on finding reinforcement learning policies for contro...
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01/26/2020
Constrained Upper Confidence Reinforcement Learning
Constrained Markov Decision Processes are a class of stochastic decision...
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07/21/2019
Online Constraint Satisfaction via Tolls in MDP Congestion Games
We consider the toll design problem that arise for a game designer of a ...
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07/08/2019
Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings
We show by counterexample that policy-gradient algorithms have no guaran...
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06/04/2019
Convergence of Learning Dynamics in Stackelberg Games
This paper investigates the convergence of learning dynamics in Stackelb...
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05/30/2019
Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings
Considering a class of gradient-based multi-agent learning algorithms in...
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04/29/2019
Competitive Statistical Estimation with Strategic Data Sources
In recent years, data has played an increasingly important role in the e...
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07/06/2018
Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences
The design of personalized incentives or recommendations to improve user...
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06/14/2018
Adaptive Incentive Design
We apply control theoretic and optimization techniques to adaptively des...
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04/16/2018
On the Convergence of Competitive, Multi-Agent Gradient-Based Learning
As learning algorithms are increasingly deployed in markets and other co...
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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...
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02/27/2018
Towards a Socially Optimal Multi-Modal Routing Platform
The increasing rate of urbanization has added pressure on the already co...
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09/25/2017
Uncertainty in Multi-Commodity Routing Networks: When does it help?
We study the equilibrium quality under user uncertainty in a multi-commo...
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03/29/2017