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07/24/2023
Contextual Bandits and Imitation Learning via Preference-Based Active Queries
We consider the problem of contextual bandits and imitation learning, wh...
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07/11/2023
Selective Sampling and Imitation Learning via Online Regression
We consider the problem of Imitation Learning (IL) by actively querying ...
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10/13/2022
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
Stochastic Gradient Descent (SGD) has been the method of choice for lear...
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06/27/2022
On the Complexity of Adversarial Decision Making
A central problem in online learning and decision making – from bandits ...
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06/19/2022
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian...
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07/11/2021
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs
Multi-epoch, small-batch, Stochastic Gradient Descent (SGD) has been the...
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06/22/2021
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
There have been many recent advances on provably efficient Reinforcement...
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12/03/2020
Online learning with dynamics: A minimax perspective
We study the problem of online learning with dynamics, where a learner i...
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06/24/2020
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations
We design an algorithm which finds an ϵ-approximate stationary point (wi...
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05/07/2020
Reinforcement Learning with Feedback Graphs
We study episodic reinforcement learning in Markov decision processes wh...
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04/09/2019
Hypothesis Set Stability and Generalization
We present an extensive study of generalization for data-dependent hypot...
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02/28/2019
Distributed Learning with Sublinear Communication
In distributed statistical learning, N samples are split across m machin...
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02/13/2019
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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10/25/2018
Uniform Convergence of Gradients for Non-Convex Learning and Optimization
We investigate 1) the rate at which refined properties of the empirical ...
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09/11/2018
Optimization with Non-Differentiable 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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06/29/2018
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Classifiers can be trained with data-dependent constraints to satisfy fa...
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04/17/2018
Two-Player Games for Efficient Non-Convex Constrained Optimization
In recent years, constrained optimization has become increasingly releva...
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03/25/2018
Logistic Regression: The Importance of Being Improper
Learning linear predictors with the logistic loss---both in stochastic a...
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03/20/2018
Online Learning: Sufficient Statistics and the Burkholder Method
We uncover a fairly general principle in online learning: If regret can ...
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11/09/2017
Small-loss bounds for online learning with partial information
We consider the problem of adversarial (non-stochastic) online learning ...
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04/13/2017
ZigZag: A new approach to adaptive online learning
We develop a novel family of algorithms for the online learning setting ...
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08/31/2016
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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02/06/2016
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits
We present efficient algorithms for the problem of contextual bandits wi...
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02/05/2016
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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12/17/2015
Private Causal Inference
Causal inference deals with identifying which random variables "cause" o...
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10/13/2015
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities
We study an equivalence of (i) deterministic pathwise statements appeari...
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08/21/2015
Adaptive Online Learning
We propose a general framework for studying adaptive regret bounds in th...
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03/04/2015
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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02/21/2015
Learning with Square Loss: Localization through Offset Rademacher Complexity
We consider regression with square loss and general classes of functions...
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01/29/2015
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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01/26/2015
Online Nonparametric Regression with General Loss Functions
This paper establishes minimax rates for online regression with arbitrar...
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01/26/2015
Online Optimization : Competing with Dynamic Comparators
Recent literature on online learning has focused on developing adaptive ...
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02/11/2014
Online Nonparametric Regression
We establish optimal rates for online regression for arbitrary classes o...
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02/12/2013
Competing With Strategies
We study the problem of online learning with a notion of regret defined ...
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08/18/2012
Online Learning with Predictable Sequences
We present methods for online linear optimization that take advantage of...
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06/27/2012
Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss
We carefully study how well minimizing convex surrogate loss functions, ...
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04/04/2012
Relax and Localize: From Value to Algorithms
We show a principled way of deriving online learning algorithms from a m...
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04/27/2011
Online Learning: Stochastic and Constrained Adversaries
Learning theory has largely focused on two main learning scenarios. The ...
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11/14/2010
Online Learning: Beyond Regret
We study online learnability of a wide class of problems, extending the ...
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10/31/2009
