62 citations · 127 across the 25 of their papers we have counts for
15 papers · 1 filter
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
Kais Hariz, Hachem Kadri, Stéphane Ayache +2
We study the implicit regularization effects of deep learning in tensor factorization. While implicit regularization in deep matrix and 'shallow' tensor factorization via linear an…
Learning primal-dual sparse kernel machines
Riikka Huusari, Sahely Bhadra, Cécile Capponi +2
Traditionally, kernel methods rely on the representer theorem which states that the solution to a learning problem is obtained as a linear combination of the data mapped into the r…
Entangled Kernels -- Beyond Separability
Riikka Huusari, Hachem Kadri
We consider the problem of operator-valued kernel learning and investigate the possibility of going beyond the well-known separable kernels. Borrowing tools and concepts from the f…
Partial Trace Regression and Low-Rank Kraus Decomposition
Hachem Kadri, Stéphane Ayache, Riikka Huusari +2
The trace regression model, a direct extension of the well-studied linear regression model, allows one to map matrices to real-valued outputs. We here introduce an even more genera…
Quantum Bandits
Balthazar Casalé, Giuseppe Di Molfetta, Hachem Kadri +1
We consider the quantum version of the bandit problem known as {\em best arm identification} (BAI). We first propose a quantum modeling of the BAI problem, which assumes that both…
Deep Networks with Adaptive Nyström Approximation
Luc Giffon, Stéphane Ayache, Thierry Artières +1
Recent work has focused on combining kernel methods and deep learning to exploit the best of the two approaches. Here, we introduce a new architecture of neural networks in which w…
Cross-view kernel transfer
Riikka Huusari, Cécile Capponi, Paul Villoutreix +1
We consider the kernel completion problem with the presence of multiple views in the data. In this context the data samples can be fully missing in some views, creating missing col…
QuicK-means: Acceleration of K-means by learning a fast transform
Luc Giffon, Valentin Emiya, Liva Ralaivola +1
K-means -- and the celebrated Lloyd algorithm -- is more than the clustering method it was originally designed to be. It has indeed proven pivotal to help increase the speed of man…
Multi-view Metric Learning in Vector-valued Kernel Spaces
Riikka Huusari, Hachem Kadri, Cécile Capponi
We consider the problem of metric learning for multi-view data and present a novel method for learning within-view as well as between-view metrics in vector-valued kernel spaces, a…
Higher-Order Low-Rank Regression
Guillaume Rabusseau, Hachem Kadri
This paper proposes an efficient algorithm (HOLRR) to handle regression tasks where the outputs have a tensor structure. We formulate the regression problem as the minimization of…
Operator-valued Kernels for Learning from Functional Response Data
Hachem Kadri, Emmanuel Duflos, Philippe Preux +3
In this paper we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of function…
Equivalence of Learning Algorithms
Julien Audiffren, Hachem Kadri
The purpose of this paper is to introduce a concept of equivalence between machine learning algorithms. We define two notions of algorithmic equivalence, namely, weak and strong eq…
Online Learning with Multiple Operator-valued Kernels
Julien Audiffren, Hachem Kadri
We consider the problem of learning a vector-valued function f in an online learning setting. The function f is assumed to lie in a reproducing Hilbert space of operator-valued ker…
Stability of Multi-Task Kernel Regression Algorithms
Julien Audiffren, Hachem Kadri
We study the stability properties of nonlinear multi-task regression in reproducing Hilbert spaces with operator-valued kernels. Such kernels, a.k.a. multi-task kernels, are approp…
Functional Regularized Least Squares Classi cation with Operator-valued Kernels
Hachem Kadri, Asma Rabaoui, Philippe Preux +2
Although operator-valued kernels have recently received increasing interest in various machine learning and functional data analysis problems such as multi-task learning or functio…