Prof. Dr. Josif Grabocka
Automatisiertes maschinelles Lernen ist der Hauptschwerpunkt der Machine Learning Forschungsgruppe. Das Team entwickelt hochmoderne Methoden, um Hyperparameter von Deep-Learning-Modellen zu optimieren – beispielsweise für große Sprachmodelle, generative Modelle, bestärkendes Lernen und neuronale Netze für tabellarische Datensätze. Dazu evaluieren wir Meta- und Transfer-Learning-Ansätze und nutzen Gray-Box-Optimierungsstrategien. Zudem konzentrieren wir uns auf vertrauenswürdiges maschinelles Lernen, wobei wir Robustheit, Fairness, Energieeffizienz und Interpretierbarkeit als zusätzliche Optimierungskriterien für Deep-Learning-Modelle berücksichtigen.
Prof. Dr. Josif Grabocka
Professur für Machine Learning
Aktuelle Forschungsprojekte
Bavarian AI Taxation Laboratory- Efficient Tax Administration through Artificial Intelligence
ReScaLe: Responsible And Scalable Learning For Robots Assisting Humans,
Carl-Zeiss Stiftung, 2022 – 2028;
Abgeschlossene Forschungsprojekte
Industriekooperationsstipendium: Automatisierte KI, Eva Mayr-Stihl Stiftung, 2019-2022
Ausgewählte Publikationen
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Guri Zabërgja, Rafiq Kamel, Arlind Kadra, Christian M. M. Frey, Josif Grabocka
End-to-End Compression for Tabular Foundation Models
International Conference on Machine Learning (ICML 2026) -
Mihail Stoian, Alexander van Renen, Jan Kobiolka, Ping-Lin Kuo, Andreas Zimmerer, Josif Grabocka, Andreas Kipf
Virtual: Compressing Data Lake Files
International Conference on Extending Database Technology (EDBT Demo 2025)
Best Demo Award -
Gresa Shala, André Biedenkapp, Pierre Krack, Florian Walter, Josif Grabocka
Efficient Cross-Episode Meta-RL
International Conference on Learning Representations (ICLR 2025) -
Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Samuel Dooley, Josif Grabocka, Frank Hutter
Multi-objective Differentiable Neural Architecture Search
International Conference on Learning Representations (ICLR 2025) -
Arlind Kadra, Sebastian Pineda Arango, Josif Grabocka
Interpretable Mesomorphic Networks
Neural Information Processing Systems (NeurIPS 2024) -
Gresa Shala, Sebastian Pineda Arango, Frank Hutter, Josif Grabocka
HPO-RL-Bench: A zero-cost benchmark for HPO in Reinforcement Learning
International Conference on Automated Machine Learning (AutoML 2024)
Runner-up Best Paper Award -
Sebastian Pineda Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka
Quickly Learning Which Pretrained Model to Finetune and How
International Conference on Learning Representations (ICLR 2024) -
Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka
Scaling Laws for Hyperparameter Optimization
Neural Information Processing Systems (NeurIPS 2023) -
Sebastian Pineda Arango, Josif Grabocka
Deep Pipeline Embeddings for AutoML
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023) -
Abdus Khazi, Sebastian Pineda Arango, Josif Grabocka
Deep Ranking Ensembles for Hyperparameter Optimization
International Conference on Learning Representations (ICLR 2023) -
Gresa Shala, Thomas Elsken, Hadi Jomaa, Frank Hutter, Josif Grabocka
Transfer NAS with Meta-Learned Bayesian Surrogates
International Conference on Learning Representations (ICLR 2023) -
Gresa Shala, Andre Biedenkapp, Frank Hutter, Josif Grabocka
Gray-Box Gaussian Processes for Automated Reinforcement Learning
International Conference on Learning Representations (ICLR 2023) -
Martin Wistuba, Arlind Kadra, Josif Grabocka
Supervising the Multi-Fidelity Race of Hyperparameter Configurations
Neural Information Processing Systems (NeurIPS 2022) -
Ekrem Öztürk, Fabio Ferreira, Hadi Jomaa, Josif Grabocka, Frank Hutter
Zero-shot AutoML with Pretrained Models
International Conference on Machine Learning (ICML 2022) -
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, Frank Hutter
Transformers Can Do Bayesian Inference
International Conference on Learning Representations (ICLR 2022) -
Sebastian Pineda Arango, Hadi Samer Jomaa, Martin Wistuba, Josif Grabocka
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
Neural Information Processing Systems, Datasets and Benchmarks Track (NeurIPS 2021) -
Arlind Kadra, Marius Lindauer, Frank Hutter, Josif Grabocka
Well-tuned Simple Nets Excel on Tabular Datasets
Neural Information Processing Systems (NeurIPS 2021) -
Michael Ruchte, Josif Grabocka
Scalable Pareto Front Approximation for Deep Multi-Objective Learning
IEEE International Conference on Data Mining (ICDM 2021) -
Ahmed Rashed, Lars Schmidt-Thieme, Josif Grabocka
A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021) -
Martin Wistuba, Josif Grabocka
Few-Shot Bayesian Optimization with Deep Kernel Surrogates
International Conference on Learning Representations (ICLR 2021) -
Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka
Dataset2vec: Learning dataset meta-features
Journal of Data Mining and Knowledge Discovery (DAMI 2020) -
Rafael R. Drumond, Lukas Brinkmeyer, Josif Grabocka, Lars Schmidt-Thieme
HIDRA: Head Initialization across Dynamic targets for Robust Architectures
SIAM International Conference on Data Mining (SDM 2020) -
Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme
Attribute-aware non-linear co-embeddings of graph features
ACM Conference on Recommender Systems (RecSys 2019) -
Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme
Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019)
Alle Publikationen
Publikationsliste von Prof. Dr. Josif Grabocka auf Google Scholar
Team
Suman Navaratnarajah
Doctoral Researcher- Phone number: +49 911 92741575
- Email: suman.navaratnarajah@utn.de
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