
What You See May Not Be What You Get: UCB Bandit Algorithms Robust to εContamination
Motivated by applications of bandit algorithms in education, we consider...
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Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles
Reinforcement learning (RL) methods have been shown to be capable of lea...
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Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning
Active learning seeks to build the best possible model with a budget of ...
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Regret Analysis of Causal Bandit Problems
We study how to learn optimal interventions sequentially given causal in...
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Nearoptimal Oracleefficient Algorithms for Stationary and NonStationary Stochastic Linear Bandits
We investigate the design of two algorithms that enjoy not only computat...
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Online Boosting for Multilabel Ranking with Topk Feedback
We present online boosting algorithms for multilabel ranking with topk ...
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On Learnability under General Stochastic Processes
Statistical learning theory under independent and identically distribute...
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Nearoptimal Reinforcement Learning in Factored MDPs: OracleEfficient Algorithms for the Nonepisodic Setting
We study reinforcement learning in factored Markov decision processes (F...
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Contextual Markov Decision Processes using Generalized Linear Models
We consider the recently proposed reinforcement learning (RL) framework ...
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Thompson Sampling in NonEpisodic Restless Bandits
Restless bandit problems assume timevarying reward distributions of the...
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Random ReLU Features: Universality, Approximation, and Composition
We propose random ReLU features models in this work. Its motivation is r...
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On Applications of Bootstrap in Continuous Space Reinforcement Learning
In decision making problems for continuous state and action spaces, line...
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Generalization Bounds in the PredictthenOptimize Framework
The predictthenoptimize framework is fundamental in many practical set...
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But How Does It Work in Theory? Linear SVM with Random Features
We prove that, under low noise assumptions, the support vector machine w...
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On the Optimality of Perturbations in Stochastic and Adversarial Multiarmed Bandit Problems
We investigate the optimality of perturbation based algorithms in the st...
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Markov Decision Processes with Continuous Side Information
We consider a reinforcement learning (RL) setting in which the agent int...
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Online Boosting Algorithms for Multilabel Ranking
We consider the multilabel ranking approach to multilabel learning. Bo...
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An ActorCritic Contextual Bandit Algorithm for Personalized Mobile Health Interventions
Increasing technological sophistication and widespread use of smartphone...
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Online Multiclass Boosting
Recent work has extended the theoretical analysis of boosting algorithms...
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Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multiarmed Bandits
Recent work on follow the perturbed leader (FTPL) algorithms for the adv...
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Sampled Fictitious Play is Hannan Consistent
Fictitious play is a simple and widely studied adaptive heuristic for pl...
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Mixture Proportion Estimation via Kernel Embedding of Distributions
Mixture proportion estimation (MPE) is the problem of estimating the wei...
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Lasso Guarantees for Time Series Estimation Under Subgaussian Tails and βMixing
Many theoretical results on estimation of high dimensional time series r...
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Fighting Bandits with a New Kind of Smoothness
We define a novel family of algorithms for the adversarial multiarmed b...
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Handling Class Imbalance in Link Prediction using Learning to Rank Techniques
We consider the link prediction problem in a partially observed network,...
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Perceptron like Algorithms for Online Learning to Rank
Perceptron is a classic online algorithm for learning a classification f...
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Consistent Algorithms for Multiclass Classification with a Reject Option
We consider the problem of nclass classification (n≥ 2), where the clas...
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On Iterative Hard Thresholding Methods for Highdimensional MEstimation
The use of Mestimators in generalized linear regression models in high ...
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Perceptronlike Algorithms and Generalization Bounds for Learning to Rank
Learning to rank is a supervised learning problem where the output space...
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On Lipschitz Continuity and Smoothness of Loss Functions in Learning to Rank
In binary classification and regression problems, it is well understood ...
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Feature Clustering for Accelerating Parallel Coordinate Descent
Largescale L1regularized loss minimization problems arise in highdime...
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The Interplay Between Stability and Regret in Online Learning
This paper considers the stability of online learning algorithms and its...
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Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret
Online learning algorithms are designed to learn even when their input i...
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Scaling Up Coordinate Descent Algorithms for Large ℓ_1 Regularization Problems
We present a generic framework for parallel coordinate descent (CD) algo...
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Orthogonal Matching Pursuit with Replacement
In this paper, we consider the problem of compressed sensing where the g...
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Online Learning: Stochastic and Constrained Adversaries
Learning theory has largely focused on two main learning scenarios. The ...
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Online Learning: Beyond Regret
We study online learnability of a wide class of problems, extending the ...
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Learning Exponential Families in HighDimensions: Strong Convexity and Sparsity
The versatility of exponential families, along with their attendant conv...
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Regularization Techniques for Learning with Matrices
There is growing body of learning problems for which it is natural to or...
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Action Centered Contextual Bandits
Contextual bandits have become popular as they offer a middle ground bet...
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Finite Time Analysis of Optimal Adaptive Policies for LinearQuadratic Systems
We consider the classical problem of control of linear systems with quad...
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On Optimality of Adaptive LinearQuadratic Regulators
Adaptive regulation of linear systems represents a canonical problem in ...
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Finite Time Adaptive Stabilization of LQ Systems
Stabilization of linear systems with unknown dynamics is a canonical pro...
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Fighting Contextual Bandits with Stochastic Smoothing
We introduce a new stochastic smoothing perspective to study adversarial...
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Online Multiclass Boosting with Bandit Feedback
We present online boosting algorithms for multiclass classification with...
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Input Perturbations for Adaptive Regulation and Learning
Design of adaptive algorithms for simultaneous regulation and estimation...
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Regret Bounds for Thompson Sampling in Restless Bandit Problems
Restless bandit problems are instances of nonstationary multiarmed ban...
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Randomized Algorithms for DataDriven Stabilization of Stochastic Linear Systems
Datadriven control strategies for dynamical systems with unknown parame...
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Ambuj Tewari
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Associate Professor of Statistics, Associate Professor of Electrical Engineering and Computer Science