Zoltan Szabo | DeepAI

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∙ 02/20/2023

Nyström M-Hilbert-Schmidt Independence Criterion

Kernel techniques are among the most popular and powerful approaches of ...

0 Florian Kalinke, et al. ∙

∙ 01/29/2023

Kernelized Cumulants: Beyond Kernel Mean Embeddings

In ℝ^d, it is well-known that cumulants provide an alternative to moment...

0 Patric Bonnier, et al. ∙

∙ 10/15/2021

Kernel Minimum Divergence Portfolios

Portfolio optimization is a key challenge in finance with the aim of cre...

0 Linda Chamakh, et al. ∙

∙ 02/09/2021

Emotion Transfer Using Vector-Valued Infinite Task Learning

Style transfer is a significant problem of machine learning with numerou...

16 Alex Lambert, et al. ∙

∙ 01/05/2021

Handling Hard Affine SDP Shape Constraints in RKHSs

Shape constraints, such as non-negativity, monotonicity, convexity or su...

0 Pierre-Cyril Aubin-Frankowski, et al. ∙

∙ 05/26/2020

Hard Shape-Constrained Kernel Machines

Shape constraints (such as non-negativity, monotonicity, convexity) play...

0 Pierre-Cyril Aubin-Frankowski, et al. ∙

∙ 10/11/2018

On Kernel Derivative Approximation with Random Fourier Features

Random Fourier features (RFF) represent one of the most popular and wide...

0 Zoltan Szabo, et al. ∙

∙ 05/22/2018

Infinite-Task Learning with Vector-Valued RKHSs

Machine learning has witnessed the tremendous success of solving tasks d...

0 Romain Brault, et al. ∙

∙ 02/13/2018

MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means

Mean embeddings provide an extremely flexible and powerful tool in machi...

0 Matthieu Lerasle, et al. ∙

∙ 08/28/2017

Characteristic and Universal Tensor Product Kernels

Kernel mean embeddings provide a versatile and powerful nonparametric re...

0 Zoltan Szabo, et al. ∙

∙ 05/22/2017

A Linear-Time Kernel Goodness-of-Fit Test

We propose a novel adaptive test of goodness-of-fit, with computational ...

0 Wittawat Jitkrittum, et al. ∙

∙ 10/15/2016

An Adaptive Test of Independence with Analytic Kernel Embeddings

A new computationally efficient dependence measure, and an adaptive stat...

0 Wittawat Jitkrittum, et al. ∙

∙ 05/22/2016

Interpretable Distribution Features with Maximum Testing Power

Two semimetrics on probability distributions are proposed, given as the ...

0 Wittawat Jitkrittum, et al. ∙

∙ 06/08/2015

Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families

We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptiv...

0 Heiko Strathmann, et al. ∙

∙ 06/06/2015

Optimal Rates for Random Fourier Features

Kernel methods represent one of the most powerful tools in machine learn...

0 Bharath K. Sriperumbudur, et al. ∙

∙ 03/09/2015

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

We propose an efficient nonparametric strategy for learning a message op...

0 Wittawat Jitkrittum, et al. ∙

∙ 11/08/2014

Learning Theory for Distribution Regression

We focus on the distribution regression problem: regressing to vector-va...

0 Zoltan Szabo, et al. ∙

∙ 10/24/2014

Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)

We introduce the Locally Linear Latent Variable Model (LL-LVM), a probab...

0 Mijung Park, et al. ∙

∙ 02/07/2014

Two-stage Sampled Learning Theory on Distributions

We focus on the distribution regression problem: regressing to a real-va...

0 Zoltan Szabo, et al. ∙

∙ 06/08/2013

Emotional Expression Classification using Time-Series Kernels

Estimation of facial expressions, as spatio-temporal processes, can take...

0 András Lőrincz, et al. ∙

∙ 10/02/2012

Distributed High Dimensional Information Theoretical Image Registration via Random Projections

Information theoretical measures, such as entropy, mutual information, a...

0 Zoltan Szabo, et al. ∙

∙ 01/01/2012

Collaborative Filtering via Group-Structured Dictionary Learning

Structured sparse coding and the related structured dictionary learning ...

0 Zoltan Szabo, et al. ∙

∙ 01/07/2007

Undercomplete Blind Subspace Deconvolution

We introduce the blind subspace deconvolution (BSSD) problem, which is t...

0 Zoltan Szabo, et al. ∙

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