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Nicole Mücke
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19
Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem
10 July 2024 by
Mattes Mollenhauer
and
others
Statistics Theory
,
Functional Analysis
Statistical inverse learning problems with random observations
23 December 2023 by
Abhishake
and
others
Statistics Theory
,
Machine Learning
How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent
14 September 2023 by
Mike Nguyen
and
Nicole Mücke
Machine Learning
Random feature approximation for general spectral methods
29 August 2023 by
Mike Nguyen
and
Nicole Mücke
Machine Learning
Local SGD in Overparameterized Linear Regression
20 October 2022 by
Mike Nguyen
and
others
Machine Learning
Algorithm Unfolding for Block-sparse and MMV Problems with Reduced Training Overhead
28 September 2022 by
Jan Christian Hauffen
and
others
Information Theory
Data splitting improves statistical performance in overparametrized regimes
21 October 2021 by
Nicole Mücke
and
others
Machine Learning
Empirical Risk Minimization in the Interpolating Regime with Application to Neural Network Learning
23 July 2021 by
Nicole Mücke
and
Ingo Steinwart
Machine Learning
From inexact optimization to learning via gradient concentration
24 June 2021 by
Bernhard Stankewitz
and
others
Machine Learning
,
Optimization and Control
Stochastic Gradient Descent Meets Distribution Regression
5 March 2021 by
Nicole Mücke
Machine Learning
Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping
18 June 2020 by
Nicole Mücke
and
Enrico Reiss
Machine Learning
,
Statistics Theory
Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces
27 May 2019 by
Ernesto Vito
and
others
Functional Analysis
,
Machine Learning
Beating SGD Saturation with Tail-Averaging and Minibatching
26 May 2019 by
Nicole Mücke
and
others
Machine Learning
Lepskii Principle in Supervised Learning
26 May 2019 by
Gilles Blanchard
and
others
Statistics Theory
Adaptivity for Regularized Kernel Methods by Lepskii's Principle
15 April 2018 by
Nicole Mücke
Machine Learning
LocalNysation: A bottom up approach to efficient localized kernel regression
17 February 2018 by
Nicole Mücke
Statistics Theory
Parallelizing Spectral Algorithms for Kernel Learning
13 November 2016 by
Gilles Blanchard
and
Nicole Mücke
Statistics Theory
,
Machine Learning
Kernel regression, minimax rates and effective dimensionality: beyond the regular case
12 November 2016 by
Gilles Blanchard
and
Nicole Mücke
Machine Learning
Optimal Rates For Regularization Of Statistical Inverse Learning Problems
14 April 2016 by
Gilles Blanchard
and
Nicole Mücke
Machine Learning
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Topics
Machine Learning
Statistics Theory
Functional Analysis
Information Theory
Probability
Optimization and Control
