
Bayesian decisionmaking under misspecified priors with applications to metalearning
Thompson sampling and other Bayesian sequential decisionmaking algorith...
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Gradient descent follows the regularization path for general losses
Recent work across many machine learning disciplines has highlighted tha...
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Practical Contextual Bandits with Regression Oracles
A major challenge in contextual bandits is to design generalpurpose alg...
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On Polynomial Time PAC Reinforcement Learning with Rich Observations
We study the computational tractability of provably sampleefficient (PA...
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Corralling a Band of Bandit Algorithms
We study the problem of combining multiple bandit algorithms (that is, o...
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Contextual Decision Processes with Low Bellman Rank are PACLearnable
This paper studies systematic exploration for reinforcement learning wit...
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Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
Highdimensional observations and complex realworld dynamics present ma...
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Unsupervised Domain Adaptation Using Approximate Label Matching
Domain adaptation addresses the problem created when training data is ge...
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Efficient and Parsimonious Agnostic Active Learning
We develop a new active learning algorithm for the streaming setting sat...
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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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Advances in Boosting (Invited Talk)
Boosting is a general method of generating many simple classification ru...
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A Bayesian Boosting Model
We offer a novel view of AdaBoost in a statistical setting. We propose a...
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Imitation Learning with a ValueBased Prior
The goal of imitation learning is for an apprentice to learn how to beha...
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Combining Spatial and Telemetric Features for Learning Animal Movement Models
We introduce a new graphical model for tracking radiotagged animals and...
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A theory of multiclass boosting
Boosting combines weak classifiers to form highly accurate predictors. A...
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The Rate of Convergence of AdaBoost
The AdaBoost algorithm was designed to combine many "weak" hypotheses th...
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A ContextualBandit Approach to Personalized News Article Recommendation
Personalized web services strive to adapt their services (advertisements...
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Analysis of boosting algorithms using the smooth margin function
We introduce a useful tool for analyzing boosting algorithms called the ...
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