
Adapting to Misspecification in Contextual Bandits
A major research direction in contextual bandits is to develop algorithm...
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Efficient FirstOrder Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
A recurring theme in statistical learning, online learning, and beyond i...
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Eluder Dimension and Generalized Rank
We study the relationship between the eluder dimension for a function cl...
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Independent Policy Gradient Methods for Competitive Reinforcement Learning
We obtain global, nonasymptotic convergence guarantees for independent ...
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Learning the Linear Quadratic Regulator from Nonlinear Observations
We introduce a new problem setting for continuous control called the LQR...
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InstanceDependent Complexity of Contextual Bandits and Reinforcement Learning: A DisagreementBased Perspective
In the classical multiarmed bandit problem, instancedependent algorith...
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Improved Bounds on Minimax Regret under Logarithmic Loss via SelfConcordance
We consider the classical problem of sequential probability assignment u...
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SecondOrder Information in NonConvex Stochastic Optimization: Power and Limitations
We design an algorithm which finds an ϵapproximate stationary point (wi...
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Open Problem: Model Selection for Contextual Bandits
In statistical learning, algorithms for model selection allow the learne...
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Learning nonlinear dynamical systems from a single trajectory
We introduce algorithms for learning nonlinear dynamical systems of the ...
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Logarithmic Regret for Adversarial Online Control
We introduce a new algorithm for online linearquadratic control in a kn...
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Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles
A fundamental challenge in contextual bandits is to develop flexible, ge...
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Naive Exploration is Optimal for Online LQR
We consider the problem of online adaptive control of the linear quadrat...
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Lower Bounds for NonConvex Stochastic Optimization
We lower bound the complexity of finding ϵstationary points (with gradi...
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ℓ_∞ Vector Contraction for Rademacher Complexity
We show that the Rademacher complexity of any R^Kvalued function class ...
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Model selection for contextual bandits
We introduce the problem of model selection for contextual bandits, wher...
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Sumofsquares meets square loss: Fast rates for agnostic tensor completion
We study tensor completion in the agnostic setting. In the classical ten...
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Hypothesis Set Stability and Generalization
We present an extensive study of generalization for datadependent hypot...
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Distributed Learning with Sublinear Communication
In distributed statistical learning, N samples are split across m machin...
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Orthogonal Statistical Learning
We provide excess risk guarantees for statistical learning in the presen...
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Uniform Convergence of Gradients for NonConvex Learning and Optimization
We investigate 1) the rate at which refined properties of the empirical ...
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Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
We introduce a new family of marginbased regret guarantees for adversar...
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Logistic Regression: The Importance of Being Improper
Learning linear predictors with the logistic lossboth in stochastic a...
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Online Learning: Sufficient Statistics and the Burkholder Method
We uncover a fairly general principle in online learning: If regret can ...
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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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Parameterfree online learning via model selection
We introduce an efficient algorithmic framework for model selection in o...
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Spectrallynormalized margin bounds for neural networks
This paper presents a marginbased multiclass generalization bound for n...
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ZigZag: A new approach to adaptive online learning
We develop a novel family of algorithms for the online learning setting ...
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Adaptive Online Learning
We propose a general framework for studying adaptive regret bounds in th...
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Dylan J. Foster
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