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09/14/2023
How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent
We analyze the generalization properties of two-layer neural networks in...
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08/29/2023
Random feature approximation for general spectral methods
Random feature approximation is arguably one of the most popular techniq...
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11/16/2022
Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem
We consider the problem of learning a linear operator θ between two Hilb...
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10/20/2022
Local SGD in Overparameterized Linear Regression
We consider distributed learning using constant stepsize SGD (DSGD) over...
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09/28/2022
Algorithm Unfolding for Block-sparse and MMV Problems with Reduced Training Overhead
In this paper we consider algorithm unfolding for the Multiple Measureme...
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10/21/2021
Data splitting improves statistical performance in overparametrized regimes
While large training datasets generally offer improvement in model perfo...
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06/09/2021
From inexact optimization to learning via gradient concentration
Optimization was recently shown to control the inductive bias in a learn...
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10/24/2020
Stochastic Gradient Descent Meets Distribution Regression
Stochastic gradient descent (SGD) provides a simple and efficient way to...
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06/18/2020
Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping
Stochastic Gradient Descent (SGD) has become the method of choice for so...
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05/27/2019
Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces
We study reproducing kernel Hilbert spaces (RKHS) on a Riemannian mani...
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05/26/2019
Lepskii Principle in Supervised Learning
In the setting of supervised learning using reproducing kernel methods, ...
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05/25/2019
Global Minima of DNNs: The Plenty Pantry
A common strategy to train deep neural networks (DNNs) is to use very la...
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02/22/2019
Beating SGD Saturation with Tail-Averaging and Minibatching
While stochastic gradient descent (SGD) is one of the major workhorses i...
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04/15/2018
Adaptivity for Regularized Kernel Methods by Lepskii's Principle
We address the problem of adaptivity in the framework of reproducing ke...
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11/12/2016
Kernel regression, minimax rates and effective dimensionality: beyond the regular case
We investigate if kernel regularization methods can achieve minimax conv...
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10/24/2016
Parallelizing Spectral Algorithms for Kernel Learning
We consider a distributed learning approach in supervised learning for a...
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04/14/2016
