Generalization of Hamiltonian algorithms

Title

Generalization of Hamiltonian algorithms

Speaker

Andreas Maurer

Abstract

The talk gives generalization results for a class of stochastic learning algorithms. The method applies whenever the algorithm generates an absolutely continuous distribution relative to some a-priori measure and the Radon Nikodym derivative has subgaussian concentration.

Applications are bounds for the Gibbs algorithm and randomizations of stable deterministic algorithms as well as PAC-Bayesian bounds with data-dependent priors.

Bio

Andreas Maurer worked in machine vision, image processing and machine learning since 1983. He is an active and independent researcher in probability theory, machine learning and statistics.

When

Thursday November 7th, 14:30

Where

Room 322, UniGe DIBRIS/DIMA, Via Dodecaneso 35