activity
20162022
most citedMKLpy: a python-based framework for Multiple Kernel Learning

16 citations · 20 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2022★ 2 cited

Novel Applications for VAE-based Anomaly Detection Systems

Luca Bergamin, Tommaso Carraro, Mirko Polato +1

The recent rise in deep learning technologies fueled innovation and boosted scientific research. Their achievements enabled new research directions for deep generative modeling (DG…

cs.LG2022

Bayes Point Rule Set Learning

Fabio Aiolli, Luca Bergamin, Tommaso Carraro +1

Interpretability is having an increasingly important role in the design of machine learning algorithms. However, interpretable methods tend to be less accurate than their black-box…

cs.LG2020★ 16 cited

MKLpy: a python-based framework for Multiple Kernel Learning

Ivano Lauriola, Fabio Aiolli

Multiple Kernel Learning is a recent and powerful paradigm to learn the kernel function from data. In this paper, we introduce MKLpy, a python-based framework for Multiple Kernel L…

cs.LG2020★ 2 cited

Conditioned Variational Autoencoder for top-N item recommendation

Tommaso Carraro, Mirko Polato, Fabio Aiolli

In this paper, we propose a Conditioned Variational Autoencoder (C-VAE) for constrained top-N item recommendation where the recommended items must satisfy a given condition. The pr…

cs.LG2018

Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning

Mirko Polato, Fabio Aiolli

A large body of research is currently investigating on the connection between machine learning and game theory. In this work, game theory notions are injected into a preference lea…

cs.IR2016

Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation

Mirko Polato, Fabio Aiolli

The increasing availability of implicit feedback datasets has raised the interest in developing effective collaborative filtering techniques able to deal asymmetrically with unambi…

cs.IR2016

Boolean kernels for collaborative filtering in top-N item recommendation

Mirko Polato, Fabio Aiolli

In many personalized recommendation problems available data consists only of positive interactions (implicit feedback) between users and items. This problem is also known as One-Cl…