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07/14/2023
Strategic Budget Selection in a Competitive Autobidding World
We study a game played between advertisers in an online ad platform. The...
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06/13/2023
Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits
We consider policy optimization in contextual bandits, where one is give...
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02/15/2023
Bandit Social Learning: Exploration under Myopic Behavior
We study social learning dynamics where the agents collectively follow a...
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01/30/2023
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics
We study a game between autobidding algorithms that compete in an online...
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11/14/2022
Efficient Contextual Bandits with Knapsacks via Regression
We consider contextual bandits with knapsacks (CBwK), a variant of the c...
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06/01/2022
Incentivizing Combinatorial Bandit Exploration
Consider a bandit algorithm that recommends actions to self-interested u...
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05/18/2022
Budget Pacing in Repeated Auctions: Regret and Efficiency without Convergence
We study the aggregate welfare and individual regret guarantees of dynam...
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03/02/2022
Truthful Online Scheduling of Cloud Workloads under Uncertainty
Cloud computing customers often submit repeating jobs and computation pi...
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02/13/2022
Incentivizing Participation in Clinical Trials
The difficulty of recruiting patients is a well-known issue in clinical ...
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10/28/2021
Sayer: Using Implicit Feedback to Optimize System Policies
We observe that many system policies that make threshold decisions invol...
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02/28/2021
Exploration and Incentives in Reinforcement Learning
How do you incentivize self-interested agents to explore when they prefe...
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07/24/2020
Beating Greedy For Approximating Reserve Prices in Multi-Unit VCG Auctions
We study the problem of finding personalized reserve prices for unit-dem...
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06/22/2020
Adaptive Discretization for Adversarial Bandits with Continuous Action Spaces
Lipschitz bandits is a prominent version of multi-armed bandits that stu...
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06/10/2020
Efficient Contextual Bandits with Continuous Actions
We create a computationally tractable algorithm for contextual bandits w...
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06/09/2020
Constrained episodic reinforcement learning in concave-convex and knapsack settings
We propose an algorithm for tabular episodic reinforcement learning with...
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05/19/2020
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits
Online learning algorithms, widely used to power search and content opti...
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02/03/2020
Sample Complexity of Incentivized Exploration
We consider incentivized exploration: a version of multi-armed bandits w...
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02/01/2020
Advances in Bandits with Knapsacks
"Bandits with Knapsacks" () is a general model for multi-armed bandits u...
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11/20/2019
Corruption Robust Exploration in Episodic Reinforcement Learning
We initiate the study of multi-stage episodic reinforcement learning und...
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04/15/2019
Introduction to Multi-Armed Bandits
Multi-armed bandits a simple but very powerful framework for algorithms ...
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02/19/2019
Bayesian Exploration with Heterogeneous Agents
It is common in recommendation systems that users both consume and produ...
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02/14/2019
The Perils of Exploration under Competition: A Computational Modeling Approach
We empirically study the interplay between exploration and competition. ...
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02/14/2019
Competing Bandits: The Perils of Exploration under Competition
We empirically study the interplay between exploration and competition. ...
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02/05/2019
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
We study contextual bandit learning with an abstract policy class and co...
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11/28/2018
Adversarial Bandits with Knapsacks
We consider Bandits with Knapsacks (henceforth, BwK), a general model fo...
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11/14/2018
Incentivizing Exploration with Unbiased Histories
In a social learning setting, there is a set of actions, each of which h...
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06/01/2018
The Externalities of Exploration and How Data Diversity Helps Exploitation
Online learning algorithms, widely used to power search and content opti...
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11/01/2014