
Consistency of Interpolation with Laplace Kernels is a HighDimensional Phenomenon
We show that minimumnorm interpolation in the Reproducing Kernel Hilber...
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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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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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Breast Tumor Cellularity Assessment using Deep Neural Networks
Breast cancer is one of the main causes of death worldwide. Histopatholo...
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On the Risk of MinimumNorm Interpolants and Restricted Lower Isometry of Kernels
We study the risk of minimumnorm interpolants of data in a Reproducing ...
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Does data interpolation contradict statistical optimality?
We show that learning methods interpolating the training data can achiev...
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Generative Modeling with Denoising AutoEncoders and Langevin Sampling
We study convergence of a generative modeling method that first estimate...
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FisherRao Metric, Geometry, and Complexity of Neural Networks
We study the relationship between geometry and capacity measures for dee...
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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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ZigZag: A new approach to adaptive online learning
We develop a novel family of algorithms for the online learning setting ...
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Nonconvex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis
Stochastic Gradient Langevin Dynamics (SGLD) is a popular variant of Sto...
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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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Inference via Message Passing on Partially Labeled Stochastic Block Models
We study the community detection and recovery problem in partiallylabel...
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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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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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Computational and Statistical Boundaries for Submatrix Localization in a Large Noisy Matrix
The interplay between computational efficiency and statistical accuracy ...
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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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Distributed Detection : Finitetime Analysis and Impact of Network Topology
This paper addresses the problem of distributed detection in multiagent...
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Geometric Inference for General HighDimensional Linear Inverse Problems
This paper presents a unified geometric framework for the statistical an...
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On ZerothOrder Stochastic Convex Optimization via Random Walks
We propose a method for zeroth order stochastic convex optimization that...
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Online Nonparametric Regression
We establish optimal rates for online regression for arbitrary classes o...
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Online Learning of Dynamic Parameters in Social Networks
This paper addresses the problem of online learning in a dynamic setting...
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Efficient Sampling from TimeVarying LogConcave Distributions
We propose a computationally efficient random walk on a convex body whic...
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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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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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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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SizeIndependent Sample Complexity of Neural Networks
We study the sample complexity of learning neural networks, by providing...
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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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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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Automatic Instrument Segmentation in RobotAssisted Surgery Using Deep Learning
Semantic segmentation of robotic instruments is an important problem for...
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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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Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks
Accurate detection and localization for angiodysplasia lesions is an imp...
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Just Interpolate: Kernel "Ridgeless" Regression Can Generalize
In the absence of explicit regularization, Kernel "Ridgeless" Regression...
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