Online AutoML in environments where the working conditions change over time.

The main goal consists of studying online optimization methods for hyper-parameter tuning.

In dynamic environments, the “optimal” hyper-parameters might change over time.

Online AutoML will consist of an exploration phase followed by an exploitation phase.

The exploration phase is looking to find the set of hyper-parameters for the current working condition. The exploitation phase will continuously monitor the learning process to detect degradation in the performance of the system which triggers a new exploitation phase.

We will consider complex problems described by pipelines where each step in the pipeline has its own hyper-parameters. We will consider problems with many hyper-parameters where some of them might be irrelevant. Among the relevant parameters, the complexity of the model architecture (with particular reference to deep networks) is of particular relevance and will be the objective of our study.

Output

1 Conference Paper

1 Journal Paper

1 Prototype software

Presentations

Project Partners:

  • INESC TEC, Joao Gama
  • Consiglio Nazionale delle Ricerche (CNR), Giuseppe Manco

 

Primary Contact: Joao Gama, INESC TEC, University of Porto

Main results of micro project:

1) Paper accepted at ECMLPKDD 2021

Hyper-Parameter Optimization for Latent Spaces in Dynamic Recommender Systems
Bruno Veloso, Luciano Caroprese, Matthias Konig, Sonia Teixeira,
Giuseppe Manco, Holger H. Hoos, and Joao Gama

2) Prototype of an Online AUTOML tool available at github

3) A prototype of a data generator used in 1)

Contribution to the objectives of HumaneAI-net WPs

The main goal of this micro-project is to develop tools that help people use sophisticated machine learning algorithms by helping in the parameterization of these algorithms.

The micro-project involved three groups from 3 different countries:
INESC TEC, Portugal
ICAR-CNR, Italy
University Leiden, Netherlands

Based on this MP, the same groups will propose two new micro-projects.
News soon!

Tangible outputs

  • Publication: Hyper-Parameter Optimization for Latent Spaces in Dynamic Recommender Systems – Bruno Veloso, Luciano Caroprese, Matthias Konig, Sonia Teixeira,
    Giuseppe Manco, Holger H. Hoos, and Joao Gama
  • Program/code: Self Hyper-parameter tunning – Bruno Veloso, Luciano Caroprese, Matthias Konig, Sonia Teixeira,
    Giuseppe Manco, Holger H. Hoos, and Joao Gama
    https://github.com/BrunoMVeloso/ECMLPKDD2021
  • Dataset: Generator for preference data – Bruno Veloso, Luciano Caroprese, Matthias Konig, Sonia Teixeira,
    Giuseppe Manco, Holger H. Hoos, and Joao Gama
    https://github.com/BrunoMVeloso/ECMLPKDD2021