62 citations · 127 across the 25 of their papers we have counts for
26 papers
Large-Scale Quantum Separability Through a Reproducible Machine Learning Lens
Balthazar Casalé, Giuseppe Di Molfetta, Sandrine Anthoine +1
The quantum separability problem consists in deciding whether a bipartite density matrix is entangled or separable. In this work, we propose a machine learning pipeline for finding…
Deep Learning with Kernels through RKHM and the Perron-Frobenius Operator
Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri
Reproducing kernel Hilbert -module (RKHM) is a generalization of reproducing kernel Hilbert space (RKHS) by means of -algebra, and the Perron-Frobenius operator is a line…
Learning in RKHM: a -Algebraic Twist for Kernel Machines
Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri
Supervised learning in reproducing kernel Hilbert space (RKHS) and vector-valued RKHS (vvRKHS) has been investigated for more than 30 years. In this paper, we provide a new twist t…
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…
Optimality conditions for spatial search with multiple marked vertices
Mathieu Roget, Hachem Kadri, Giuseppe Di Molfetta
We contribute to fulfil the long-lasting gap in the understanding of the spatial search with multiple marked vertices. The theoretical framework is that of discrete-time quantum wa…
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…
Quantum Perceptron Revisited: Computational-Statistical Tradeoffs
Mathieu Roget, Giuseppe Di Molfetta, Hachem Kadri
Quantum machine learning algorithms could provide significant speed-ups over their classical counterparts; however, whether they could also achieve good generalization remains uncl…
Implicit Regularization in Deep Tensor Factorization
Paolo Milanesi, Hachem Kadri, Stéphane Ayache +1
Attempts of studying implicit regularization associated to gradient descent (GD) have identified matrix completion as a suitable test-bed. Late findings suggest that this phenomeno…
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…
Mapping individual differences in cortical architecture using multi-view representation learning
Akrem Sellami, François-Xavier Dupé, Bastien Cagna +4
In neuroscience, understanding inter-individual differences has recently emerged as a major challenge, for which functional magnetic resonance imaging (fMRI) has proven invaluable.…
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…
M-Power Regularized Least Squares Regression
Julien Audiffren, Hachem Kadri
Regularization is used to find a solution that both fits the data and is sufficiently smooth, and thereby is very effective for designing and refining learning algorithms. But the…
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…
Multiple functional regression with both discrete and continuous covariates
Hachem Kadri, Philippe Preux, Emmanuel Duflos +1
In this paper we present a nonparametric method for extending functional regression methodology to the situation where more than one functional covariate is used to predict a funct…
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…