Can Fairness be Automated?

Posted on April 3, 2023 by Hilde Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Eddie Bergman, Noor Awad, Mykola Pechenizkiy, Joaquin Vanschoren, Bernd Bischl, Frank Hutter

At the risk of sounding cliché, “with great power comes great responsibility.” While we don’t want to suggest that machine learning (ML) practitioners are superheroes, what was true for Spiderman is also true for those building predictive models – and even more so for those building AutoML tools. Only last year, the Netherlands Institute for […]

Wrapping Up AutoML-Conf 2022 and Introducing the 2023 Edition

Posted on December 21, 2022 by Frank Hutter, Marius Lindauer

The inaugural AutoML conference 2022 was an exciting adventure for us! With 170 attendees in the very first iteration, we assess this conference as a big success and are confirmed in our belief that it was the right time to transition from a workshop series to a full-fledged conference. In this blogpost, we will summarize […]

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Posted on October 11, 2022 by Samuel Müller, Katharina Eggensperger, Frank Hutter

A radically new approach to tabular classification: we introduce TabPFN, a new tabular data classification method that takes < 1 second & yields SOTA performance (competitive with the best AutoML pipelines in an hour). So far, it is limited in scale, though: it can only tackle problems up to 1000 training examples, 100 features and […]

Deep Learning 2.0: Extending the Power of Deep Learning to the Meta-Level

Posted on March 17, 2022 by Frank Hutter

Deep Learning (DL) has been able to revolutionize learning from raw data (images, text, speech, etc) by replacing domain-specific hand-crafted features with features that are jointly learned for the particular task at hand. In this blog post, I propose to take deep learning to the next level, by also jointly (meta-)learning other, currently hand-crafted, elements […]

Introducing Reproducibility Reviews

Posted on February 17, 2022 by Frank Hutter

By Frank Hutter, Isabelle Guyon, Marius Lindauer and Mihaela van der Schaar (general and program chairs of AutoML-Conf 2022) Did you ever try to reproduce a paper from a top ML conference and failed to do so? You’re not alone! At AutoML-Conf (see automl.cc), we’re aiming for a higher standard: with the papers we publish […]

Announcing the Automated Machine Learning Conference 2022

Posted on December 3, 2021 by Frank Hutter, Marius Lindauer

Modern machine learning systems come with many design decisions (including hyperparameters, architectures of neural networks and the entire data processing pipeline), and the idea of automating these decisions gave rise to the research field of automated machine learning (AutoML). AutoML has been booming over the last decade, with hundreds of papers published each year now […]

Neural Ensemble Search for Uncertainty Estimation and Dataset Shift

Posted on December 1, 2020 by Arber Zela, Frank Hutter

In many real world scenarios, deep learning models such as neural networks are deployed to make predictions on data coming from a shifted distribution (aka covariate shift) or out-of-distribution (OOD) data not at all represented in the training set. Examples include blurred or noisy images, unknown objects in images or videos, a new frequency band […]

Auto-PyTorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL

Posted on November 27, 2020 by Lucas Zimmer, Marius Lindauer, Frank Hutter

By Auto-PyTorch is a framework for automated deep learning (AutoDL) that uses BOHB as a backend to optimize the full deep learning pipeline, including data preprocessing, network training techniques and regularization methods. Auto-PyTorch is the successor of AutoNet which was one of the first frameworks to perform this joint optimization.

NAS-Bench-301 and the Case for Surrogate NAS Benchmarks

Posted on October 9, 2020 by Julien Siems, Lucas Zimmer, Arber Zela, Jovita Lukasik, Margret Keuper, Frank Hutter

The Need for Realistic NAS Benchmarks Neural Architecture Search (NAS) is a logical next step in representation learning as it removes human bias from architecture design, similar to deep learning removing human bias from feature engineering. As such, NAS has experienced rapid growth in recent years, leading to state-of-the-art performance on many tasks. However, empirical […]

Our seven 2019 papers on neural architecture search (NAS)

Posted on October 21, 2019 by Frank Hutter

By Neural Architecture Search (NAS) is a very hot topic in AutoML these days, and our group is very actively publishing in this area. We have seven NAS papers in 2019, which may make us one of the world’s most active groups in NAS (only closely surpassed by a small company called Google ;-). Here […]