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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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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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Efficient Contextual Bandits with Continuous Actions
We create a computationally tractable algorithm for contextual bandits w...
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Constrained episodic reinforcement learning in concave-convex and knapsack settings
We propose an algorithm for tabular episodic reinforcement learning with...
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Greedy Algorithm almost Dominates in Smoothed Contextual Bandits
Online learning algorithms, widely used to power search and content opti...
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Sample Complexity of Incentivized Exploration
We consider incentivized exploration: a version of multi-armed bandits w...
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Advances in Bandits with Knapsacks
"Bandits with Knapsacks" () is a general model for multi-armed bandits u...
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Corruption Robust Exploration in Episodic Reinforcement Learning
We initiate the study of multi-stage episodic reinforcement learning und...
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Introduction to Multi-Armed Bandits
Multi-armed bandits a simple but very powerful framework for algorithms ...
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Bayesian Exploration with Heterogeneous Agents
It is common in recommendation systems that users both consume and produ...
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The Perils of Exploration under Competition: A Computational Modeling Approach
We empirically study the interplay between exploration and competition. ...
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Competing Bandits: The Perils of Exploration under Competition
We empirically study the interplay between exploration and competition. ...
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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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Adversarial Bandits with Knapsacks
We consider Bandits with Knapsacks (henceforth, BwK), a general model fo...
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Incentivizing Exploration with Unbiased Histories
In a social learning setting, there is a set of actions, each of which h...
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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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How Many Workers to Ask? Adaptive Exploration for Collecting High Quality Labels
Crowdsourcing has been part of the IR toolbox as a cheap and fast mechan...
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