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07/28/2023
Weighted variation spaces and approximation by shallow ReLU networks
We investigate the approximation of functions f on a bounded domain Ω⊂ℝ^...
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06/16/2023
LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning
Labeled data are critical to modern machine learning applications, but o...
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05/25/2023
Vector-Valued Variation Spaces and Width Bounds for DNNs: Insights on Weight Decay Regularization
Deep neural networks (DNNs) trained to minimize a loss term plus the sum...
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01/23/2023
Deep Learning Meets Sparse Regularization: A Signal Processing Perspective
Deep learning has been wildly successful in practice and most state-of-t...
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10/06/2022
A Better Way to Decay: Proximal Gradient Training Algorithms for Neural Nets
Weight decay is one of the most widely used forms of regularization in d...
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09/18/2021
Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks
We study the problem of estimating an unknown function from noisy data u...
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05/07/2021
What Kinds of Functions do Deep Neural Networks Learn? Insights from Variational Spline Theory
We develop a variational framework to understand the properties of funct...
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06/10/2020
Neural Networks, Ridge Splines, and TV Regularization in the Radon Domain
We develop a variational framework to understand the properties of the f...
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02/03/2020
Optimal Confidence Regions for the Multinomial Parameter
Construction of tight confidence regions and intervals is central to sta...
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10/05/2019
Minimum "Norm" Neural Networks are Splines
We develop a general framework based on splines to understand the interp...
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05/29/2019
Active Learning in the Overparameterized and Interpolating Regime
Overparameterized models that interpolate training data often display su...
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09/18/2018
Concentration Inequalities for the Empirical Distribution
We study concentration inequalities for the Kullback--Leibler (KL) diver...
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04/26/2018
Tensor Methods for Nonlinear Matrix Completion
In the low rank matrix completion (LRMC) problem, the low rank assumptio...
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03/28/2017
Algebraic Variety Models for High-Rank Matrix Completion
We consider a generalization of low-rank matrix completion to the case w...
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03/09/2015
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion
Low-rank matrix completion (LRMC) problems arise in a wide variety of ap...
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10/02/2014
Deterministic Conditions for Subspace Identifiability from Incomplete Sampling
Consider a generic r-dimensional subspace of R^d, r<d, and suppose that ...
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04/13/2014
Active Learning for Undirected Graphical Model Selection
This paper studies graphical model selection, i.e., the problem of estim...
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06/26/2013
Near-Optimal Adaptive Compressed Sensing
This paper proposes a simple adaptive sensing and group testing algorith...
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09/11/2012
Query Complexity of Derivative-Free Optimization
This paper provides lower bounds on the convergence rate of Derivative F...
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09/06/2012
The Sample Complexity of Search over Multiple Populations
This paper studies the sample complexity of searching over multiple popu...
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09/16/2011
Active Ranking using Pairwise Comparisons
This paper examines the problem of ranking a collection of objects using...
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04/22/2011
Convex Approaches to Model Wavelet Sparsity Patterns
Statistical dependencies among wavelet coefficients are commonly represe...
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10/22/2009
