Automated Machine Learning

TNT members involved in this project:
Carolin Benjamins, M.Sc.
Difan Deng, M. Sc.
MSc. Theresa Eimer
Prof. Dr. rer. nat. Marius Lindauer
René Sass

To use machine learning (ML), users have to choose between many design options: (i) ML algorithms (ii) pre-processing techniques, (iii) post-processing techniques, (iv) hyperparameter settings, (v) architectures of neural networks and so on. These design decisions are often responsible whether ML systems return random predictions or achieve state-of-the-art performance. Unfortunately, even for ML-experts it is a tedious and error-prone task and thus it is not easy to make these decisions efficiently.

Automated machine learning (AutoML) addresses this challenge by automating the design process such that AutoML tools support users to efficiently develop new ML applications.

Hyperparameter Optimization and Bayesian Optimization

To achieve peak-performance with an algorithm, choosing an appropriate hyperparameter configuration is crucial. Since hyperparameters are often not very intuitive for human developers, it is a tedious and error-prone task to choose these settings. Bayesian Optimization is a sample-efficient approach to find such hyperparameter configurations in an automatic way, saving human developers tremendous amounts of development time.

Neural Architecture Search

Applying deep learning to new datasets also requires to find a well-performing architecture of a deep neural network. Such an architecture influences the performance, but also other metrics, such as inference time, memory consumption etc pp. Unfortunately, it is again not obvious for human developers how to design such deep neural networks making the process fairly inefficient. Neural architecture search is an paradigma to automatically determine the best architectures for new datasets, making new applications of deep learning feasible also at larger scale.

Dynamic Algorithm Configuration

Instead of choosing the hyperparameters of an ML algorithm once, many hyperparameters have to be adapted over time. A well-known example is the learning rate of a deep neural network, which is decreased, sometimes also increased, over time. So far, these dynamic hyperparameters are controlled by a human-designed heuristic, which is often not optimal for a new dataset. Therefore, we develop new approaches for dynamic algorithm configuration, which learns from data how to adjust these on-the-fly.

Interpretability of AutoML 

A major drawback of AutoML tools is the risk that ML will be even a more mysterious black box than it ever was. Therefore, we also develop analysis tools that provide feedback to AutoML users about important insights, such as, (i) how to use AutoML tools more efficiently or (ii) which hyperparameter decisions were important to achieve the final performance. This helps ML developers to get a better understanding of why and how ML and AutoML works.

Show all publications
  • Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor Awad, Marius Lindauer, Frank Hutter
    HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
    Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track), December 2021
  • Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl
    Explaining Hyperparameter Optimization via Partial Dependence Plots
    Proceedings of the international conference on Neural Information Processing Systems (NeurIPS), December 2021
  • Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka
    Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data
    Proceedings of the international conference on Neural Information Processing Systems (NeurIPS), December 2021