Luca Martino

Viterbi Classifier Chains for Multi-Dimensional Learning

2015 · viXra

Multi-dimensional classification (also known variously as multi-target, multi-objective, and multi-output classification) is the supervised learning problem where an instance is associated to qualitative discrete variables (a.k.a. labels), rather than with a single class, as in …

A new strategy for effective learning in population Monte Carlo sampling

2016

In this work, we focus on advancing the theory and practice of a class of Monte Carlo methods, population Monte Carlo (PMC) sampling, for dealing with inference problems with static parameters. We devise a new …

Anti-tempered layered adaptive importance sampling

2017

Monte Carlo (MC) methods are widely used for Bayesian inference in signal processing, machine learning and statistics. In this work, we introduce an adaptive importance sampler which mixes together the benefits of the Importance Sampling …