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02/28/2023
Time-uniform confidence bands for the CDF under nonstationarity
Estimation of the complete distribution of a random variable is a useful...
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02/17/2023
Graph Feedback via Reduction to Regression
When feedback is partial, leveraging all available information is critic...
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02/16/2023
Infinite Action Contextual Bandits with Reusable Data Exhaust
For infinite action contextual bandits, smoothed regret and reduction to...
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11/28/2022
Personalized Reward Learning with Interaction-Grounded Learning (IGL)
In an era of countless content offerings, recommender systems alleviate ...
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11/14/2022
Towards Data-Driven Offline Simulations for Online Reinforcement Learning
Modern decision-making systems, from robots to web recommendation engine...
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10/25/2022
Eigen Memory Tree
This work introduces the Eigen Memory Tree (EMT), a novel online memory ...
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10/24/2022
Deploying a Steered Query Optimizer in Production at Microsoft
Modern analytical workloads are highly heterogeneous and massively compl...
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10/24/2022
Conditionally Risk-Averse Contextual Bandits
We desire to apply contextual bandits to scenarios where average-case st...
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10/20/2022
A lower confidence sequence for the changing mean of non-negative right heavy-tailed observations with bounded mean
A confidence sequence (CS) is an anytime-valid sequential inference prim...
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10/19/2022
Anytime-valid off-policy inference for contextual bandits
Contextual bandit algorithms are ubiquitous tools for active sequential ...
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07/12/2022
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces
Designing efficient general-purpose contextual bandit algorithms that wo...
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07/12/2022
Contextual Bandits with Large Action Spaces: Made Practical
A central problem in sequential decision making is to develop algorithms...
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06/16/2022
Interaction-Grounded Learning with Action-inclusive Feedback
Consider the problem setting of Interaction-Grounded Learning (IGL), in ...
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06/13/2021
Bellman-consistent Pessimism for Offline Reinforcement Learning
The use of pessimism, when reasoning about datasets lacking exhaustive e...
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06/09/2021
Interaction-Grounded Learning
Consider a prosthetic arm, learning to adapt to its user's control signa...
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06/09/2021
ChaCha for Online AutoML
We propose the ChaCha (Champion-Challengers) algorithm for making an onl...
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06/01/2021
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Offline RL
We study session-based recommendation scenarios where we want to recomme...
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02/18/2021
Off-policy Confidence Sequences
We develop confidence bounds that hold uniformly over time for off-polic...
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06/07/2019
Empirical Likelihood for Contextual Bandits
We apply empirical likelihood techniques to contextual bandit policy val...
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05/06/2019
Lessons from Real-World Reinforcement Learning in a Customer Support Bot
In this work, we describe practical lessons we have learned from success...
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07/17/2018
Contextual Memory Trees
We design and study a Contextual Memory Tree (CMT), a learning memory co...
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06/15/2016
Logarithmic Time One-Against-Some
We create a new online reduction of multiclass classification to binary ...
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02/05/2016
Active Information Acquisition
We propose a general framework for sequential and dynamic acquisition of...
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11/10/2015
A Hierarchical Spectral Method for Extreme Classification
Extreme classification problems are multiclass and multilabel classifica...
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11/13/2014
A Randomized Algorithm for CCA
We present RandomizedCCA, a randomized algorithm for computing canonical...
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08/09/2014
Normalized Online Learning
We introduce online learning algorithms which are independent of feature...
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10/07/2013
Discriminative Features via Generalized Eigenvectors
Representing examples in a way that is compatible with the underlying cl...
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06/07/2013