Machine Learning Lab

Prof. Dr. Josif Grabocka

Automated Machine Learning is the primary focus of the Machine Learning Lab. In particular, the team is interested in developing cutting-edge methods for efficiently tuning the hyperparameters of Deep Learning models, including but not limited to optimizing the settings of Large Language Models, Generative Models, Reinforcement Learning policies, and neural networks for tabular datasets. We tackle the optimization of hyperparameters via meta- and transfer-learning approaches from prior evaluations and gray-box optimization strategies. In addition, we also focus on trustworthy Machine Learning, where we consider robustness, fairness, energy efficiency, and interpretability, as auxiliary optimization criteria for Deep Learning models.

Prof. Dr. Josif Grabocka
Professor of Machine Learning

Current Research Projects

ReScaLe: Responsible And Scalable Learning For Robots Assisting Humans,
Carl-Zeiss Stiftung, 2022 – 2028;

View Project

SFB „Small Data“,
DFG, 2023-2027;

View Project

Completed Research Projects

Industry Cooperation Grant: Automated AI, Eva Mayr-Stihl Stiftung, 2019-2022

Selected Publications

  • 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)

All Publications

List of Publications of Prof. Dr. Josif Grabocka on Google Scholar

List of Publications of Prof. Dr. Josif Grabocka on dblp

GitHub Repository of the Machine Learning Lab

Team

Dr. Christian Frey

Postdoctoral Researcher

Maciej Janowski

Doctoral Researcher (external)

Arlind Kadra

Doctoral Researcher (external)

Jan Kobiolka

Doctoral Researcher

Gresa Shala

Doctoral Researcher (external)

Guri Zabergja

Doctoral Researcher (external)

You still have questions?

Then contact our Machine Learning Lab.

 machine-learning@utn.de