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06/26/2023
Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits
This paper is motivated by recent developments in the linear bandit lite...
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10/01/2022
Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms
Multi-armed bandit (MAB) algorithms are efficient approaches to reduce t...
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06/20/2022
Thompson Sampling Efficiently Learns to Control Diffusion Processes
Diffusion processes that evolve according to linear stochastic different...
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10/21/2021
Learning to Recommend Using Non-Uniform Data
Learning user preferences for products based on their past purchases or ...
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02/16/2021
The Randomized Elliptical Potential Lemma with an Application to Linear Thompson Sampling
In this note, we introduce a randomized version of the well-known ellipt...
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06/11/2020
On Worst-case Regret of Linear Thompson Sampling
In this paper, we consider the worst-case regret of Linear Thompson Samp...
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02/24/2020
Optimal and Greedy Algorithms for Multi-Armed Bandits with Many Arms
We characterize Bayesian regret in a stochastic multi-armed bandit probl...
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02/12/2020
A General Framework to Analyze Stochastic Linear Bandit
In this paper we study the well-known stochastic linear bandit problem w...
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11/09/2019
Optimal Experimental Design for Staggered Rollouts
Experimentation has become an increasingly prevalent tool for guiding po...
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04/18/2019
On Low-rank Trace Regression under General Sampling Distribution
A growing number of modern statistical learning problems involve estimat...
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10/27/2017
Matrix Completion Methods for Causal Panel Data Models
In this paper we develop new methods for estimating causal effects in se...
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04/28/2017
Exploiting the Natural Exploration In Contextual Bandits
The contextual bandit literature has traditionally focused on algorithms...
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11/21/2016
Scalable Approximations for Generalized Linear Problems
In stochastic optimization, the population risk is generally approximate...
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04/25/2016
