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

Project Partners:

  • INESC TEC, University of Porto, Joao Gama
  • INESC TEC, Joao Gama
  • CNR, Giuseppe Manco

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