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06/15/2023
Large-Scale Quantum Separability Through a Reproducible Machine Learning Lens
The quantum separability problem consists in deciding whether a bipartit...
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05/23/2023
Deep Learning with Kernels through RKHM and the Perron-Frobenius Operator
Reproducing kernel Hilbert C^*-module (RKHM) is a generalization of repr...
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10/21/2022
Learning in RKHM: a C^*-Algebraic Twist for Kernel Machines
Supervised learning in reproducing kernel Hilbert space (RKHS) and vecto...
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07/18/2022
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
We study the implicit regularization effects of deep learning in tensor ...
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01/30/2022
Spatial search with multiple marked vertices is optimal for almost all queries and its quantum advantage is not always guaranteed
We contribute to fulfil the long-lasting gap in the understanding of the...
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08/27/2021
Learning primal-dual sparse kernel machines
Traditionally, kernel methods rely on the representer theorem which stat...
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06/04/2021
Quantum Perceptron Revisited: Computational-Statistical Tradeoffs
Quantum machine learning algorithms could provide significant speed-ups ...
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05/04/2021
Implicit Regularization in Deep Tensor Factorization
Attempts of studying implicit regularization associated to gradient desc...
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01/14/2021
Entangled Kernels – Beyond Separability
We consider the problem of operator-valued kernel learning and investiga...
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07/02/2020
Partial Trace Regression and Low-Rank Kraus Decomposition
The trace regression model, a direct extension of the well-studied linea...
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04/01/2020
Mapping individual differences in cortical architecture using multi-view representation learning
In neuroscience, understanding inter-individual differences has recently...
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02/15/2020
Quantum Bandits
We consider the quantum version of the bandit problem known as best arm...
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11/29/2019
Deep Networks with Adaptive Nyström Approximation
Recent work has focused on combining kernel methods and deep learning to...
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10/14/2019
Kernel transfer over multiple views for missing data completion
We consider the kernel completion problem with the presence of multiple ...
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08/23/2019
QuicK-means: Acceleration of K-means by learning a fast transform
K-means -- and the celebrated Lloyd algorithm -- is more than the cluste...
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03/21/2018
Multi-view Metric Learning in Vector-valued Kernel Spaces
We consider the problem of metric learning for multi-view data and prese...
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10/28/2015
Operator-valued Kernels for Learning from Functional Response Data
In this paper we consider the problems of supervised classification and ...
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10/09/2013
M-Power Regularized Least Squares Regression
Regularization is used to find a solution that both fits the data and is...
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06/17/2013
Stability of Multi-Task Kernel Regression Algorithms
We study the stability properties of nonlinear multi-task regression in ...
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01/12/2013
Multiple functional regression with both discrete and continuous covariates
In this paper we present a nonparametric method for extending functional...
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01/12/2013
Functional Regularized Least Squares Classi cation with Operator-valued Kernels
Although operator-valued kernels have recently received increasing inter...
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05/10/2012
A Generalized Kernel Approach to Structured Output Learning
We study the problem of structured output learning from a regression per...
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03/07/2012