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05/23/2023
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
The success of over-parameterized neural networks trained to near-zero t...
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12/23/2022
Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
Linear partial differential equations (PDEs) are an important, widely ap...
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06/23/2022
Utilizing Expert Features for Contrastive Learning of Time-Series Representations
We present an approach that incorporates expert knowledge for time-serie...
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03/17/2022
A Framework and Benchmark for Deep Batch Active Learning for Regression
We study the performance of different pool-based Batch Mode Deep Active ...
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10/19/2021
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning
Pruning neural networks reduces inference time and memory costs. On stan...
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09/20/2021
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
Artificial neural networks (NNs) are one of the most frequently used mac...
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07/16/2021
Intrinsic Dimension Adaptive Partitioning for Kernel Methods
We prove minimax optimal learning rates for kernel ridge regression, res...
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11/04/2020
Which Minimizer Does My Neural Network Converge To?
The loss surface of an overparameterized neural network (NN) possesses m...
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03/13/2020
Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension
We derive improved regression and classification rates for support vecto...
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03/13/2020
SVM Learning Rates for Data with Low Intrinsic Dimension
We derive improved regression and classification rates for support vecto...
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02/12/2020
Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent
We prove that two-layer (Leaky)ReLU networks initialized by e.g. the wid...
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02/08/2020
Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space
Given an uncountable, compact metric space, we show that there exists no...
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09/22/2019
PAC-Bayesian Bounds for Deep Gaussian Processes
Variational approximation techniques and inference for stochastic models...
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05/27/2019
Best-scored Random Forest Classification
We propose an algorithm named best-scored random forest for binary class...
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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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05/04/2019
Improved Classification Rates for Localized SVMs
One of the main characteristics of localized support vector machines tha...
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03/27/2019
A Sober Look at Neural Network Initializations
Initializing the weights and the biases is a key part of the training pr...
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10/04/2018
Optimal Learning with Anisotropic Gaussian SVMs
This paper investigates the nonparametric regression problem using SVMs ...
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12/14/2017
Strictly proper kernel scores and characteristic kernels on compact spaces
Strictly proper kernel scores are well-known tool in probabilistic forec...
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08/17/2017
Adaptive Clustering Using Kernel Density Estimators
We investigate statistical properties of a clustering algorithm that rec...
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02/24/2017
Learning Rates for Kernel-Based Expectile Regression
Conditional expectiles are becoming an increasingly important tool in fi...
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02/23/2017
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm
Learning rates for regularized least-squares algorithms are in most case...
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02/22/2017
liquidSVM: A Fast and Versatile SVM package
liquidSVM is a package written in C++ that provides SVM-type solvers for...
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07/13/2016
Kernel Density Estimation for Dynamical Systems
We study the density estimation problem with observations generated by c...
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05/10/2016
Learning theory estimates with observations from general stationary stochastic processes
This paper investigates the supervised learning problem with observation...
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08/21/2015
Representation of Quasi-Monotone Functionals by Families of Separating Hyperplanes
We characterize when the level sets of a continuous quasi-monotone funct...
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08/15/2015
Towards an Axiomatic Approach to Hierarchical Clustering of Measures
We propose some axioms for hierarchical clustering of probability measur...
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09/30/2014
Fully adaptive density-based clustering
The clusters of a distribution are often defined by the connected compon...
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08/14/2007
Fast rates for support vector machines using Gaussian kernels
For binary classification we establish learning rates up to the order of...
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07/02/2007