
Online learning with dynamics: A minimax perspective
We study the problem of online learning with dynamics, where a learner i...
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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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Reinforcement Learning with Feedback Graphs
We study episodic reinforcement learning in Markov decision processes wh...
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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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The Complexity of Making the Gradient Small in Stochastic Convex Optimization
We give nearly matching upper and lower bounds on the oracle complexity ...
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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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Optimization with NonDifferentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals
We show that many machine learning goals, such as improved fairness metr...
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Training WellGeneralizing Classifiers for Fairness Metrics and Other DataDependent Constraints
Classifiers can be trained with datadependent constraints to satisfy fa...
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TwoPlayer Games for Efficient NonConvex Constrained Optimization
In recent years, constrained optimization has become increasingly releva...
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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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Smallloss bounds for online learning with partial information
We consider the problem of adversarial (nonstochastic) online learning ...
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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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A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks
We revisit the elegant observation of T. Cover '65 which, perhaps, is no...
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BISTRO: An Efficient RelaxationBased Method for Contextual Bandits
We present efficient algorithms for the problem of contextual bandits wi...
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Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters
The amount of data available in the world is growing faster than our abi...
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Private Causal Inference
Causal inference deals with identifying which random variables "cause" o...
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On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities
We study an equivalence of (i) deterministic pathwise statements appeari...
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Adaptive Online Learning
We propose a general framework for studying adaptive regret bounds in th...
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Hierarchies of Relaxations for Online Prediction Problems with Evolving Constraints
We study online prediction where regret of the algorithm is measured aga...
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Learning with Square Loss: Localization through Offset Rademacher Complexity
We consider regression with square loss and general classes of functions...
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Sequential Probability Assignment with Binary Alphabets and Large Classes of Experts
We analyze the problem of sequential probability assignment for binary o...
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Online Nonparametric Regression with General Loss Functions
This paper establishes minimax rates for online regression with arbitrar...
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Online Optimization : Competing with Dynamic Comparators
Recent literature on online learning has focused on developing adaptive ...
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Online Nonparametric Regression
We establish optimal rates for online regression for arbitrary classes o...
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Competing With Strategies
We study the problem of online learning with a notion of regret defined ...
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Online Learning with Predictable Sequences
We present methods for online linear optimization that take advantage of...
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Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss
We carefully study how well minimizing convex surrogate loss functions, ...
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Relax and Localize: From Value to Algorithms
We show a principled way of deriving online learning algorithms from a m...
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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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