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Found 26 papers, 3 papers with code
$C^*$-Algebraic Machine Learning: Moving in a New Direction
Machine learning has a long collaborative tradition with several fields of mathematics, such as statistics, probability and linear algebra.
Orthogonal Random Features: Explicit Forms and Sharp Inequalities
Random features have been introduced to scale up kernel methods via randomization techniques.
Large-Scale Quantum Separability Through a Reproducible Machine Learning Lens
The quantum separability problem consists in deciding whether a bipartite density matrix is entangled or separable.
Learning in RKHM: a $C^*$-Algebraic Twist for Kernel Machines
Supervised learning in reproducing kernel Hilbert space (RKHS) and vector-valued RKHS (vvRKHS) has been investigated for more than 30 years.
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
We study the implicit regularization effects of deep learning in tensor factorization.
Learning primal-dual sparse kernel machines
In this paper, instead of using the traditional representer theorem, we propose to search for a solution in RKHS that has a pre-image decomposition in the original data space, where the elements don't necessarily correspond to the elements in the training set.
Quantum Perceptron Revisited: Computational-Statistical Tradeoffs
Quantum machine learning algorithms could provide significant speed-ups over their classical counterparts; however, whether they could also achieve good generalization remains unclear.
Implicit Regularization in Deep Tensor Factorization
Attempts of studying implicit regularization associated to gradient descent (GD) have identified matrix completion as a suitable test-bed.
Entangled Kernels -- Beyond Separability
We consider the problem of operator-valued kernel learning and investigate the possibility of going beyond the well-known separable kernels.
Sparse matrix products for neural network compression
Over-parameterization of neural networks is a well known issue that comes along with their great performance.
Partial Trace Regression and Low-Rank Kraus Decomposition
The trace regression model, a direct extension of the well-studied linear regression model, allows one to map matrices to real-valued outputs.
Mapping individual differences in cortical architecture using multi-view representation learning
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
We consider the quantum version of the bandit problem known as {\em best arm identification} (BAI).
Deep Networks with Adaptive Nyström Approximation
Recent work has focused on combining kernel methods and deep learning to exploit the best of the two approaches.
Cross-view kernel transfer
We consider the kernel completion problem with the presence of multiple views in the data.
QuicK-means: Acceleration of K-means by learning a fast transform
K-means -- and the celebrated Lloyd algorithm -- is more than the clustering method it was originally designed to be.
Multi-view Metric Learning in Vector-valued Kernel Spaces
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, as a way to capture multi-modal structure of the data.
Low-Rank Regression with Tensor Responses
This paper proposes an efficient algorithm (HOLRR) to handle regression tasks where the outputs have a tensor structure.
Higher-Order Low-Rank Regression
This paper proposes an efficient algorithm (HOLRR) to handle regression tasks where the outputs have a tensor structure.
Operator-valued Kernels for Learning from Functional Response Data
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 functions, and the label is also a function.
Equivalence of Learning Algorithms
The purpose of this paper is to introduce a concept of equivalence between machine learning algorithms.
Online Learning with Multiple Operator-valued Kernels
We consider the problem of learning a vector-valued function f in an online learning setting.
M-Power Regularized Least Squares Regression
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.
Stability of Multi-Task Kernel Regression Algorithms
We show that multi-task kernel regression algorithms are uniformly stable in the general case of infinite-dimensional output spaces.
Multiple Operator-valued Kernel Learning
We study this problem in the case of kernel ridge regression for functional responses with an lr-norm constraint on the combination coefficients.
A Generalized Kernel Approach to Structured Output Learning
Finally, we evaluate the performance of our KDE approach using both covariance and conditional covariance kernels on two structured output problems, and compare it to the state-of-the-art kernel-based structured output regression methods.
