AutoML School 2024
By increasing the efficiency of ML-application development and supporting users in crucial design decisions, AutoML became a key approach in the toolkit of many developers and researchers. Although there is an exponentially growing interest in AutoML, AutoML is so far only rarely taught at universities and there is a large gap between the current state of the art in research and disseminated knowledge. The AutoML Summer School will cover core topics of AutoML, covering basics, state-of-the-art approaches and hands-on sessions. Enthusiastic AutoML experts will present their diverse views on AutoML to ML practitioners, developers, research engineers, researchers and students.
18
2024–2025
137
13 Stunden 0 Minuten
18 Ergebnisse
13:20
4Wever, Marcel2024Leibniz Universität Hannover (LUH) et al.
59:41
14Rijn, Jan vanAutomated Machine Learning (AutoML) is a relatively young research area aiming at making high-performance machine learning techniques accessible to a broad set of users. This is achieved by identifying all design choices in creating a machine-learning model and addressing them automatically to generate performance-optimised models. In this opening lecture, we provide an extensive overview of the past and present, as well as future perspectives of AutoML. First, we introduce the concept of AutoML, formally define the problems it aims to solve and describe the three components underlying AutoML approaches: the search space, search strategy and performance evaluation. Next, we discuss hyperparameter optimisation (HPO) techniques commonly used in AutoML systems design, followed by providing an overview of the neural architecture search, a particular case of AutoML for automatically generating deep learning models. We further review and compare available AutoML systems. Finally, we provide a list of open challenges and future research directions.
2024Leibniz Universität Hannover (LUH) et al.
48:36
32Shchur, OleksandrTime series forecasting is an essential component of decision-making in domains such as energy, retail, and finance. Traditionally, machine learning practitioners have focused on developing task-specific forecasting models that are restricted to a certain dataset or application domain. Inspired by the success of pretrained Large Language Models (LLMs) in natural language processing, it becomes imperative to explore whether a similar approach can be applied to forecasting: Can we train a single large model on huge amounts of diverse time series data, that will generalize to new unseen time series tasks? In this talk, we introduce Chronos, a family of pretrained forecasting models based on minimal modifications to LLM architectures, that accomplishes this goal. Chronos demonstrates remarkable zero-shot performance on unseen datasets, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines. We discuss open challenges in the development and application of pretrained models for time series forecasting, and explore what role AutoML methods can play in overcoming them.
2024Leibniz Universität Hannover (LUH) et al.
42:36
9Biedenkapp, André et al.2024Leibniz Universität Hannover (LUH) et al.
50:02
8Strecker, KatharinaIn numerous scientific disciplines, ML methods offers significant potential for advancing research in novel ways. However, a considerable challenge for domain experts lies in the necessity for extensive ML experience to effectively apply these methods. Similarly, the Centre for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW) encountered this obstacle when attempting to integrate ML into the work of their researchers. This talk will demonstrate how the ZSW applied AutoML to create new opportunities for renewable energy domain experts who lacked ML expertise. The session will also illustrate the use of AutoML through a variety of examples in this domain and present the ZSW's self-developed no-code AutoML tool, KI-Lab.EE.
2024Leibniz Universität Hannover (LUH) et al.
25:15
6Theodorakopoulos, Daphne2024Leibniz Universität Hannover (LUH) et al.
1:13:14
4Kersting, Kristian2024Leibniz Universität Hannover (LUH) et al.
51:31
Wagner, Markus2024Leibniz Universität Hannover (LUH) et al.
1:12:02
Bringmann, Oliver et al.2025Leibniz Universität Hannover (LUH) et al.
13:55
1Benjamins, Carolin2025Leibniz Universität Hannover (LUH) et al.
12:46
12Purucker, Lennart2025Leibniz Universität Hannover (LUH) et al.
09:33
9Benjamins, Carolin et al.2025Leibniz Universität Hannover (LUH) et al.