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 […]
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 […]
Posted on November 26, 2021 by Aditya Mohan, Carolin Benjamins
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 […]
Posted on September 17, 2021 by Katharina Eggensperger
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 […]
Posted on August 18, 2021 by Samuel Müller
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. […]
Posted on July 12, 2021 by Theresa Eimer
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 […]
Posted on June 24, 2021 by Theresa Eimer
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 […]
Posted on April 19, 2021 by Raghu Rajan, André Biedenkapp, Frederic Runge, Jörg Franke
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 […]
Posted on February 26, 2021 by Katharina Eggensperger, Matthias Feurer
2020 is over. Time to look back at the amazing major features we introduced to Auto-Sklearn.
Posted on December 10, 2020 by Guest
By Koen van der Blom, Holger Hoos, Alex Serban, Joost Visser In our global survey among teams that build ML applications, we found ample room for increased adoption of AutoML techniques. While AutoML is adopted at least partially by more than 70% of teams in research labs and tech companies, for teams in non-tech and […]
