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07/08/2023
Efficient Model-Free Exploration in Low-Rank MDPs
A major challenge in reinforcement learning is to develop practical, sam...
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06/02/2023
Convex and Non-Convex Optimization under Generalized Smoothness
Classical analysis of convex and non-convex optimization methods often r...
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05/29/2023
On the Variance, Admissibility, and Stability of Empirical Risk Minimization
It is well known that Empirical Risk Minimization (ERM) with squared los...
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05/01/2023
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring
A central problem in the theory of multi-agent reinforcement learning (M...
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04/27/2023
Convergence of Adam Under Relaxed Assumptions
In this paper, we provide a rigorous proof of convergence of the Adaptiv...
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04/12/2023
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL
We study the design of sample-efficient algorithms for reinforcement lea...
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03/06/2023
Lower Bounds for γ-Regret via the Decision-Estimation Coefficient
In this note, we give a new lower bound for the γ-regret in bandit probl...
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02/10/2023
Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making
Smoothed online learning has emerged as a popular framework to mitigate ...
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11/25/2022
A Note on Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
We consider the problem of interactive decision making, encompassing str...
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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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05/04/2022
Rate of convergence of the smoothed empirical Wasserstein distance
Consider an empirical measure ℙ_n induced by n iid samples from a d-dime...
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02/15/2022
Damped Online Newton Step for Portfolio Selection
We revisit the classic online portfolio selection problem, where at each...
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02/09/2022
Smoothed Online Learning is as Easy as Statistical Learning
Much of modern learning theory has been split between two regimes: the c...
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12/27/2021
The Statistical Complexity of Interactive Decision Making
A fundamental challenge in interactive learning and decision making, ran...
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12/03/2021
On Submodular Contextual Bandits
We consider the problem of contextual bandits where actions are subsets ...
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06/08/2021
Intrinsic Dimension Estimation
It has long been thought that high-dimensional data encountered in many ...
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03/16/2021
Deep learning: a statistical viewpoint
The remarkable practical success of deep learning has revealed some majo...
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02/24/2021
On the Minimal Error of Empirical Risk Minimization
We study the minimal error of the Empirical Risk Minimization (ERM) proc...
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02/15/2021
Top-k eXtreme Contextual Bandits with Arm Hierarchy
Motivated by modern applications, such as online advertisement and recom...
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10/08/2020
Learning the Linear Quadratic Regulator from Nonlinear Observations
We introduce a new problem setting for continuous control called the LQR...
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10/07/2020
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
In the classical multi-armed bandit problem, instance-dependent algorith...
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06/19/2020
Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis
Recently, the task of image generation has attracted much attention. In ...
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06/19/2020
Fast Mixing of Multi-Scale Langevin Dynamics underthe Manifold Hypothesis
Recently, the task of image generation has attracted much attention. In ...
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06/07/2020
On Suboptimality of Least Squares with Application to Estimation of Convex Bodies
We develop a technique for establishing lower bounds on the sample compl...
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04/30/2020
Learning nonlinear dynamical systems from a single trajectory
We introduce algorithms for learning nonlinear dynamical systems of the ...
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02/12/2020
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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01/31/2020
Generative Modeling with Denoising Auto-Encoders and Langevin Sampling
We study convergence of a generative modeling method that first estimate...
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11/15/2019
ℓ_∞ Vector Contraction for Rademacher Complexity
We show that the Rademacher complexity of any R^K-valued function class ...
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08/27/2019
On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels
We study the risk of minimum-norm interpolants of data in a Reproducing ...
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05/05/2019
Breast Tumor Cellularity Assessment using Deep Neural Networks
Breast cancer is one of the main causes of death worldwide. Histopatholo...
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12/28/2018
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon
We show that minimum-norm interpolation in the Reproducing Kernel Hilber...
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08/01/2018
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize
In the absence of explicit regularization, Kernel "Ridgeless" Regression...
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06/25/2018
Does data interpolation contradict statistical optimality?
We show that learning methods interpolating the training data can achiev...
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04/21/2018
Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks
Accurate detection and localization for angiodysplasia lesions is an imp...
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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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03/03/2018
Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning
Semantic segmentation of robotic instruments is an important problem for...
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02/02/2018
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
Breast cancer is one of the main causes of cancer death worldwide. Early...
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01/07/2018
Theory of Deep Learning IIb: Optimization Properties of SGD
In Theory IIb we characterize with a mix of theory and experiments the o...
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12/18/2017
Size-Independent Sample Complexity of Neural Networks
We study the sample complexity of learning neural networks, by providing...
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12/13/2017
Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
Skeletal bone age assessment is a common clinical practice to diagnose e...
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11/05/2017
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks
We study the relationship between geometry and capacity measures for dee...
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09/12/2017
Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information
We study the misclassification error for community detection in general ...
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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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02/13/2017
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis
Stochastic Gradient Langevin Dynamics (SGLD) is a popular variant of Sto...
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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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03/22/2016
Inference via Message Passing on Partially Labeled Stochastic Block Models
We study the community detection and recovery problem in partially-label...
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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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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