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05/27/2022
Provably Sample-Efficient RL with Side Information about Latent Dynamics
We study reinforcement learning (RL) in settings where observations are ...
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05/06/2022
Convex Analysis at Infinity: An Introduction to Astral Space
Not all convex functions on ℝ^n have finite minimizers; some can only be...
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07/03/2021
Bayesian decision-making under misspecified priors with applications to meta-learning
Thompson sampling and other Bayesian sequential decision-making algorith...
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06/19/2020
Gradient descent follows the regularization path for general losses
Recent work across many machine learning disciplines has highlighted tha...
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03/03/2018
Practical Contextual Bandits with Regression Oracles
A major challenge in contextual bandits is to design general-purpose alg...
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03/01/2018
On Polynomial Time PAC Reinforcement Learning with Rich Observations
We study the computational tractability of provably sample-efficient (PA...
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12/19/2016
Corralling a Band of Bandit Algorithms
We study the problem of combining multiple bandit algorithms (that is, o...
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10/29/2016
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable
This paper studies systematic exploration for reinforcement learning wit...
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03/14/2016
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
High-dimensional observations and complex real-world dynamics present ma...
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02/16/2016
Unsupervised Domain Adaptation Using Approximate Label Matching
Domain adaptation addresses the problem created when training data is ge...
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06/29/2015
Efficient and Parsimonious Agnostic Active Learning
We develop a new active learning algorithm for the streaming setting sat...
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02/04/2014
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
We present a new algorithm for the contextual bandit learning problem, w...
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12/12/2012
Advances in Boosting (Invited Talk)
Boosting is a general method of generating many simple classification ru...
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09/10/2012
A Bayesian Boosting Model
We offer a novel view of AdaBoost in a statistical setting. We propose a...
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06/20/2012
Imitation Learning with a Value-Based Prior
The goal of imitation learning is for an apprentice to learn how to beha...
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03/15/2012
Combining Spatial and Telemetric Features for Learning Animal Movement Models
We introduce a new graphical model for tracking radio-tagged animals and...
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08/15/2011
A theory of multiclass boosting
Boosting combines weak classifiers to form highly accurate predictors. A...
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06/29/2011
The Rate of Convergence of AdaBoost
The AdaBoost algorithm was designed to combine many "weak" hypotheses th...
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02/28/2010
A Contextual-Bandit Approach to Personalized News Article Recommendation
Personalized web services strive to adapt their services (advertisements...
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03/28/2008