research
∙
02/20/2023
Nyström M-Hilbert-Schmidt Independence Criterion
Kernel techniques are among the most popular and powerful approaches of ...
share
research
∙
01/29/2023
Kernelized Cumulants: Beyond Kernel Mean Embeddings
In ℝ^d, it is well-known that cumulants provide an alternative to moment...
research
∙
10/15/2021
Kernel Minimum Divergence Portfolios
Portfolio optimization is a key challenge in finance with the aim of cre...
research
∙
02/09/2021
Emotion Transfer Using Vector-Valued Infinite Task Learning
Style transfer is a significant problem of machine learning with numerou...
research
∙
01/05/2021
Handling Hard Affine SDP Shape Constraints in RKHSs
Shape constraints, such as non-negativity, monotonicity, convexity or su...
research
∙
05/26/2020
Hard Shape-Constrained Kernel Machines
Shape constraints (such as non-negativity, monotonicity, convexity) play...
research
∙
10/11/2018
On Kernel Derivative Approximation with Random Fourier Features
Random Fourier features (RFF) represent one of the most popular and wide...
research
∙
05/22/2018
Infinite-Task Learning with Vector-Valued RKHSs
Machine learning has witnessed the tremendous success of solving tasks d...
research
∙
02/13/2018
MONK -- Outlier-Robust Mean Embedding Estimation by Median-of-Means
Mean embeddings provide an extremely flexible and powerful tool in machi...
research
∙
08/28/2017
Characteristic and Universal Tensor Product Kernels
Kernel mean embeddings provide a versatile and powerful nonparametric re...
research
∙
05/22/2017
A Linear-Time Kernel Goodness-of-Fit Test
We propose a novel adaptive test of goodness-of-fit, with computational ...
research
∙
10/15/2016
An Adaptive Test of Independence with Analytic Kernel Embeddings
A new computationally efficient dependence measure, and an adaptive stat...
research
∙
05/22/2016
Interpretable Distribution Features with Maximum Testing Power
Two semimetrics on probability distributions are proposed, given as the ...
research
∙
06/08/2015
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptiv...
research
∙
06/06/2015
Optimal Rates for Random Fourier Features
Kernel methods represent one of the most powerful tools in machine learn...
research
∙
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...
research
∙
11/08/2014
Learning Theory for Distribution Regression
We focus on the distribution regression problem: regressing to vector-va...
research
∙
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...
research
∙
02/07/2014
Two-stage Sampled Learning Theory on Distributions
We focus on the distribution regression problem: regressing to a real-va...
research
∙
06/08/2013
Emotional Expression Classification using Time-Series Kernels
Estimation of facial expressions, as spatio-temporal processes, can take...
research
∙
10/02/2012
Distributed High Dimensional Information Theoretical Image Registration via Random Projections
Information theoretical measures, such as entropy, mutual information, a...
research
∙
01/01/2012
Collaborative Filtering via Group-Structured Dictionary Learning
Structured sparse coding and the related structured dictionary learning ...
research
∙
01/07/2007
