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Masterarbeit:

Deep Learning for Algorithm Selection

Im Rahmen des Projekts AutoML

Beschreibung

Algorithm selection addresses the problem of automatically determining the best algorithm for a given task (often called instance or input to the algorithm). A well known approach is to train a machine learning model which predicts for a given instance which algorithm to use. Previous studies showed that this can lead to automatic performance improvements between factor 4 and 10. Although there exist many different machine learning approaches to solve the algorithm selection problem, how to use deep learning in this context is rarely studied. The task in this project is to (i) use AutoML approaches to find a well-suited architecture for different algorithm selection datasets, (ii) study different loss functions for algorithm selection and (iii) run large scale experiments on ASlib. The project will be based on AutoFolio and its implementation.

Voraussetzungen

We strongly recommend that students should have hands-on experience for the following topics:

To apply for this project, please send us an email with the following information:
  1. Which ML-related courses have you taken?
  2. Can you please attach your transcript of records?
  3. Which projects have you done so far (in Hannover and elsewhere)?
  4. Which topics interest you most?


  5. Since different projects require different skill sets, please also rate your skills in the following categories on a scale from ++ (very good) to -- (no knowledge/skill):
  6. Creativity / ideas for developing new algorithms
  7. Getting someone else's large code base to run
  8. Running comprehensive experimental studies / keeping track of results
  9. Self-motivation to push through even if things don't work for a while
  10. Coding skills
    1. Python
    2. TensorFlow
    3. Keras
    4. PyTorch
    5. C/C++
  11. Ability to read a RL paper, implement it and get it to work
  12. Ability to read a DL paper, implement it and get it to work
  13. Ansprechpartner: Marius Lindauer