AutoML.org

Freiburg-Hannover

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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DACBench: Benchmarking Dynamic Algorithm Configuration

Dynamic Algorithm Configuration (DAC) has been shown to significantly improve algorithm performance over static or even handcrafted dynamic hyperparameter policies [Biedenkapp et al., 2020]. Most algorithms, however, are not designed with DAC in mind and have to be adapted to be controlled online. This requires a great deal of familiarity with the target algorithm as […]

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AutoRL: AutoML for RL

Reinforcement learning (RL) has shown impressive results in a variety of applications. Well known examples include game and video game playing, robotics and, recently, “Autonomous navigation of stratospheric balloons”. A lot of the successes came about by combining the expressiveness of deep learning with the power of RL. Already on their own though, both frameworks […]

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