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04/12/2023
Possibility-theoretic statistical inference offers performance and probativeness assurances
Statisticians are largely focused on developing methods that perform wel...
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06/07/2022
Group Meritocratic Fairness in Linear Contextual Bandits
We study the linear contextual bandit problem where an agent has to sele...
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05/30/2022
Meta Representation Learning with Contextual Linear Bandits
Meta-learning seeks to build algorithms that rapidly learn how to solve ...
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02/21/2022
Multi-task Representation Learning with Stochastic Linear Bandits
We study the problem of transfer-learning in the setting of stochastic l...
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12/19/2021
Valid inferential models for prediction in supervised learning problems
Prediction, where observed data is used to quantify uncertainty about a ...
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12/19/2021
Approximately valid probabilistic inference on a class of statistical functionals
Existing frameworks for probabilistic inference assume the inferential t...
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12/07/2020
Online Model Selection: a Rested Bandit Formulation
Motivated by a natural problem in online model selection with bandit inf...
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05/18/2020
Meta-learning with Stochastic Linear Bandits
We investigate meta-learning procedures in the setting of stochastic lin...
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01/24/2020
Valid distribution-free inferential models for prediction
A fundamental problem in statistics and machine learning is that of usin...
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10/07/2019
Stochastic Bandits with Delay-Dependent Payoffs
Motivated by recommendation problems in music streaming platforms, we pr...
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09/28/2018