AutoML.org

Freiburg-Hannover-Tübingen

Rethinking AutoML: Advancing from a Machine-Centered to Human-Centered Paradigm

In this blog post, we argue why the development of the first generation of AutoML tools ended up being less fruitful than expected and how we envision a new paradigm of automated machine learning (AutoML) that is focused on the needs and workflows of ML practitioners and data scientists. The Vision of AutoML The last […]

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

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 […]

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DEHB

DEHB: EVOLUTIONARY HYPERBAND FOR SCALABLE, ROBUST AND EFFICIENT HYPERPARAMETER OPTIMIZATION By Noor Awad, Modern machine learning algorithms crucially rely on several design decisions to achieve strong performance, making the problem of Hyperparameter Optimization (HPO) more important than ever. We believe that a practical, general HPO method must fulfill many desiderata, including: (1) strong anytime performance, […]

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

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 […]

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Introducing Reproducibility Reviews

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 […]

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Announcing the Automated Machine Learning Conference 2022

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 […]

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CARL: A benchmark to study generalization in Reinforcement Learning

TL;DR: CARL is a benchmark for contextual RL (cRL). In cRL, we aim to generalize over different contexts. In CARL we saw that if we vary the context, the learning becomes more difficult, and making the context explicit can facilitate learning. CARL makes the context defining the behavior of the environment visible and configurable. This […]

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HPOBench: Compare Multi-fidelity Optimization Algorithms with Ease

When researching and developing new hyperparameter optimization (HPO) methods, a good collection of benchmark problems, ideally relevant, realistic and cheap-to-evaluate, is a very valuable resource. While such collections exist for synthetic problems (COCO) or simple HPO problems (Bayesmark), to the best of our knowledge there is no such collection for multi-fidelity benchmarks. With ever-growing machine […]

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TrivialAugment: You don’t need to tune your augmentations for image classification

Strong image classification models need augmentations. That is consensus in the community for a few years now. Some augmentation choices became standard over the time for some datasets, but the question what augmentations strategy is optimal for a given dataset remained. This opened the opportunity of doing hyper-parameter optimization (HPO) to find optimal augmentation choices. […]

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Self-Paced Context Evaluation for Contextual Reinforcement Learning

RL agents, just like humans, often benefit from a difficulty curve in learning [Matiisen et al. 2017, Fuks et al. 2019, Zhang et al. 2020]. Progressing from simple task instances, e.g. walking on flat surfaces or towards goals that are very close to the agent, to more difficult ones lets the agent accomplish much harder […]

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