Transfer Bayesian Meta-Learning Via Weighted Free Energy Minimization

Yunchuan Zhang, Sharu Theresa Jose, Osvaldo Simeone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Meta-learning optimizes the hyperparameters of a training procedure, such as its initialization, kernel, or learning rate, based on data sampled from a number of auxiliary tasks. A key underlying assumption is that the auxiliary tasks-known as meta-Training tasks-share the same generating distribution as the tasks to be encountered at deployment time-known as meta-Test tasks. This may, however, not be the case when the test environment differ from the meta-Training conditions. To address shifts in task generating distribution between meta-Training and meta-Testing phases, this paper introduces weighted free energy minimization (WFEM) for transfer meta-learning. We instantiate the proposed approach for non-parametric Bayesian regression and classification via Gaussian Processes (GPs). The method is validated on a toy sinusoidal regression problem, as well as on classification using miniImagenet and CUB data sets, through comparison with standard meta-learning of GP priors as implemented by PACOH.

Original languageEnglish (US)
Title of host publication2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781728163383
DOIs
StatePublished - 2021
Event31st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2021 - Gold Coast, Australia
Duration: Oct 25 2021Oct 28 2021

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2021-October
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference31st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2021
Country/TerritoryAustralia
CityGold Coast
Period10/25/2110/28/21

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing

Keywords

  • Bayesian learning
  • Gaussian Process
  • Transfer Meta-learning

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