Aleksandrs Slivkins

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Microsoft

Beating Greedy For Approximating Reserve Prices in Multi-Unit VCG Auctions

We study the problem of finding personalized reserve prices for unit-dem...
07/24/2020 ∙ by Mahsa Derakhshan, et al. ∙ 0 ∙ share

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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...
06/22/2020 ∙ by Chara Podimata, et al. ∙ 0 ∙ share

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Efficient Contextual Bandits with Continuous Actions

We create a computationally tractable algorithm for contextual bandits w...
06/10/2020 ∙ by Maryam Majzoubi, et al. ∙ 0 ∙ share

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Constrained episodic reinforcement learning in concave-convex and knapsack settings

We propose an algorithm for tabular episodic reinforcement learning with...
06/09/2020 ∙ by Kianté Brantley, et al. ∙ 8 ∙ share

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Greedy Algorithm almost Dominates in Smoothed Contextual Bandits

Online learning algorithms, widely used to power search and content opti...
05/19/2020 ∙ by Manish Raghavan, et al. ∙ 9 ∙ share

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Sample Complexity of Incentivized Exploration

We consider incentivized exploration: a version of multi-armed bandits w...
02/03/2020 ∙ by Mark Sellke, et al. ∙ 0 ∙ share

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Advances in Bandits with Knapsacks

"Bandits with Knapsacks" () is a general model for multi-armed bandits u...
02/01/2020 ∙ by Karthik Abinav Sankararaman, et al. ∙ 0 ∙ share

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Corruption Robust Exploration in Episodic Reinforcement Learning

We initiate the study of multi-stage episodic reinforcement learning und...
11/20/2019 ∙ by Thodoris Lykouris, et al. ∙ 12 ∙ share

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Introduction to Multi-Armed Bandits

Multi-armed bandits a simple but very powerful framework for algorithms ...
04/15/2019 ∙ by Aleksandrs Slivkins, et al. ∙ 0 ∙ share

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Bayesian Exploration with Heterogeneous Agents

It is common in recommendation systems that users both consume and produ...
02/19/2019 ∙ by Nicole Immorlica, et al. ∙ 0 ∙ share

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The Perils of Exploration under Competition: A Computational Modeling Approach

We empirically study the interplay between exploration and competition. ...
02/14/2019 ∙ by Guy Aridor, et al. ∙ 0 ∙ share

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Competing Bandits: The Perils of Exploration under Competition

We empirically study the interplay between exploration and competition. ...
02/14/2019 ∙ by Guy Aridor, et al. ∙ 0 ∙ share

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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...
02/05/2019 ∙ by Akshay Krishnamurthy, et al. ∙ 38 ∙ share

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Adversarial Bandits with Knapsacks

We consider Bandits with Knapsacks (henceforth, BwK), a general model fo...
11/28/2018 ∙ by Nicole Immorlica, et al. ∙ 0 ∙ share

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Incentivizing Exploration with Unbiased Histories

In a social learning setting, there is a set of actions, each of which h...
11/14/2018 ∙ by Nicole Immorlica, et al. ∙ 0 ∙ share

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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...
06/01/2018 ∙ by Manish Raghavan, et al. ∙ 0 ∙ share

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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...
11/01/2014 ∙ by Ittai Abraham, et al. ∙ 0 ∙ share

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Aleksandrs Slivkins

Featured Co-authors

Akshay Krishnamurthy

John Langford

Zhiwei Steven Wu

Wen Sun

Max Simchowitz

Miroslav Dudík

Nicole Immorlica

Chicheng Zhang

Jennifer Wortman Vaughan

Jieming Mao

David M. Pennock

Beating Greedy For Approximating Reserve Prices in Multi-Unit VCG Auctions

Adaptive Discretization for Adversarial Bandits with Continuous Action Spaces

Efficient Contextual Bandits with Continuous Actions

Constrained episodic reinforcement learning in concave-convex and knapsack settings

Greedy Algorithm almost Dominates in Smoothed Contextual Bandits

Sample Complexity of Incentivized Exploration

Advances in Bandits with Knapsacks

Corruption Robust Exploration in Episodic Reinforcement Learning

Introduction to Multi-Armed Bandits

Bayesian Exploration with Heterogeneous Agents

The Perils of Exploration under Competition: A Computational Modeling Approach

Competing Bandits: The Perils of Exploration under Competition

Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting

Adversarial Bandits with Knapsacks

Incentivizing Exploration with Unbiased Histories

The Externalities of Exploration and How Data Diversity Helps Exploitation

How Many Workers to Ask? Adaptive Exploration for Collecting High Quality Labels