Michaël Fanuel | DeepAI

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∙ 05/25/2023

On sampling determinantal and Pfaffian point processes on a quantum computer

DPPs were introduced by Macchi as a model in quantum optics the 1970s. S...

0 Rémi Bardenet, et al. ∙

∙ 10/15/2022

Smoothing complex-valued signals on Graphs with Monte-Carlo

We introduce new smoothing estimators for complex signals on graphs, bas...

0 Hugo Jaquard, et al. ∙

∙ 08/31/2022

Sparsification of the regularized magnetic Laplacian with multi-type spanning forests

In this paper, we consider a U(1)-connection graph, that is, a graph whe...

0 Michaël Fanuel, et al. ∙

∙ 12/02/2021

Recovering Hölder smooth functions from noisy modulo samples

In signal processing, several applications involve the recovery of a fun...

0 Michaël Fanuel, et al. ∙

∙ 06/27/2021

Nonparametric estimation of continuous DPPs with kernel methods

Determinantal Point Process (DPPs) are statistical models for repulsive ...

5 Michaël Fanuel, et al. ∙

∙ 05/28/2021

Towards Deterministic Diverse Subset Sampling

Determinantal point processes (DPPs) are well known models for diverse s...

0 Joachim Schreurs, et al. ∙

∙ 04/06/2021

Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks

Commonly, machine learning models minimize an empirical expectation. As ...

0 Joachim Schreurs, et al. ∙

∙ 11/13/2020

Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems

Semi-parametric regression models are used in several applications which...

0 Michaël Fanuel, et al. ∙

∙ 09/10/2020

Denoising modulo samples: k-NN regression and tightness of SDP relaxation

Many modern applications involve the acquisition of noisy modulo samples...

0 Michaël Fanuel, et al. ∙

∙ 06/24/2020

Ensemble Kernel Methods, Implicit Regularization and Determinental Point Processes

By using the framework of Determinantal Point Processes (DPPs), some the...

0 Joachim Schreurs, et al. ∙

∙ 06/16/2020

The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks

Generative Adversarial Networks (GANs) are performant generative methods...

23 Hannes De Meulemeester, et al. ∙

∙ 06/12/2020

Disentangled Representation Learning and Generation with Manifold Optimization

Disentanglement is an enjoyable property in representation learning whic...

34 Arun Pandey, et al. ∙

∙ 02/20/2020

Diversity sampling is an implicit regularization for kernel methods

Kernel methods have achieved very good performance on large scale regres...

0 Michaël Fanuel, et al. ∙

∙ 02/05/2020

Wasserstein Exponential Kernels

In the context of kernel methods, the similarity between data points is ...

0 Henri De Plaen, et al. ∙

∙ 05/29/2019

Nyström landmark sampling and regularized Christoffel functions

Selecting diverse and important items from a large set is a problem of i...

0 Michaël Fanuel, et al. ∙

∙ 11/20/2017

Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions

In machine learning or statistics, it is often desirable to reduce the d...

0 Michaël Fanuel, et al. ∙

∙ 10/21/2016

Convex Formulation for Kernel PCA and its Use in Semi-Supervised Learning

In this paper, Kernel PCA is reinterpreted as the solution to a convex o...

0 Carlos M. Alaíz, et al. ∙

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