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02/03/2022
Multiclass learning with margin: exponential rates with no bias-variance trade-off
We study the behavior of error bounds for multiclass classification unde...
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01/17/2022
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression
Kernel methods provide a principled approach to nonparametric learning. ...
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10/21/2021
Mean Nyström Embeddings for Adaptive Compressive Learning
Compressive learning is an approach to efficient large scale learning ba...
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09/20/2021
Understanding neural networks with reproducing kernel Banach spaces
Characterizing the function spaces corresponding to neural networks can ...
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06/11/2021
Learning the optimal regularizer for inverse problems
In this work, we consider the linear inverse problem y=Ax+ϵ, where A X→ ...
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06/17/2020
Regularized ERM on random subspaces
We study a natural extension of classical empirical risk minimization, w...
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06/17/2020
Interpolation and Learning with Scale Dependent Kernels
We study the learning properties of nonparametric ridge-less least squar...
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07/08/2019
Multi-Scale Vector Quantization with Reconstruction Trees
We propose and study a multi-scale approach to vector quantization. We d...
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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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09/23/2018
A Learning Theory Approach to a Computationally Efficient Parameter Selection for the Elastic Net
Despite recent advances in regularisation theory, the issue of parameter...
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07/26/2016
Scale Invariant Interest Points with Shearlets
Shearlets are a relatively new directional multi-scale framework for sig...
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04/16/2012