Profile – Machine Learning Lab

PhD Student, Tabular Data

Lennart Purucker

Postal address

Institut für Informatik
Albert-Ludwigs-Universität Freiburg
Sekretariat Hutter/Maschinelles Lernen
Georges-Köhler-Allee 074
79110 Freiburg, Germany

Office

Building 074, Room 00-012

purucker@cs.uni-freiburg.de

Publications

About

I am a Ph.D. student at the University of Freiburg, Germany, supervised by Frank Hutter. My Ph.D. position is part of the Small Data Initiative (CRC 1597, Project C05). My research interest is in the field of artificial intelligence, with a focus on automated machine learning, ensemble learning, deep learning, and meta-learning (for small data). My primary focus is on tabular data (e.g., Excel sheets), but I also work on vision, text, and time series data.

I completed my bachelor’s degree in applied computer science in 2019 at the DHBW Stuttgart and my master’s degree in computer science in 2021 at the RWTH Aachen. From November 2021 to August 2023, I worked as a research assistant at the University of Siegen on ensemble learning for automated machine learning (AutoML) and recommender systems. From August 2023 to November 2023, I was an applied scientist intern at AWS as part of the AutoGluon team.

In 2024, I mainly worked on TabPFN, a foundation model for (small) tabular data. Furthermore, I participated in Kaggle's AutoML Grand Prix, as leader of the “AutoML Grandmasters” team, where we scored a very close (1 point) second place with AutoGluon and TabPFN to win $20,000.

Other

Community Involvement:

Reproducibility Chair at the AutoML Conference 2023, 2024, and 2025.
Co-organizer of the AutoML Seminar
Developer of AutoGluon (Tabular)
Member of the OpenML Team (Python API)

Reviewing:

2024: AutoML, NeurIPS DBT, CVPR Workshop on Foundation Models for Medical Vision, ICML ICL Workshop
2023: Reproducibility Reviewer at the AutoML Conference

Teaching:

Publications

2024

Arango, Sebastian Pineda; Janowski, Maciej; Purucker, Lennart; Zela, Arber; Hutter, Frank; Grabocka, Josif

Ensembling Finetuned Language Models for Text Classification Inproceedings

In: NeurIPS 2024 Workshop on Fine-Tuning in Modern Machine Learning: Principles and Scalability, 2024.

Küken, Jaris; Purucker, Lennart; Hutter, Frank

Large Language Models Engineer Too Many Simple Features for Tabular Data Inproceedings

In: NeurIPS 2024 Third Table Representation Learning Workshop, 2024, (Oral Presentation).

Sukthanker, Rhea Sanjay; Zela, Arber; Staffler, Benedikt; Klein, Aaron; Purucker, Lennart; Franke, Joerg K. H.; Hutter, Frank

HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models Inproceedings

In: 38th Conference on Neural Information Processing Systems (NeurIPS), DBT Track, 2024.

Strangmann, Tobias; Purucker, Lennart; Franke, Jörg K. H.; Rapant, Ivo; Ferreira, Fabio; Hutter, Frank

Transfer Learning for Finetuning Large Language Models Inproceedings

In: NeurIPS 2024 Workshop on Adaptive Foundation Models, 2024.

Arango, Sebastian Pineda; Janowski, Maciej; Purucker, Lennart; Zela, Arber; Hutter, Frank; Grabocka, Josif

Dynamic Post-Hoc Neural Ensemblers Inproceedings

In: Preprint, 2024.

Rapant, Ivo; Purucker, Lennart; Ferreira, Fabio; Arango, Sebastian Pineda; Kadra, Arlind; Grabocka, Josif; Hutter, Frank

Quick-Tune-Tool: A Practical Tool and its User Guide for Automatically Finetuning Pretrained Models Inproceedings

In: Proceedings of the Third International Conference on Automated Machine Learning (AutoML 2024), Workshop Track, 2024.

Maier, Jannis; Möller, Felix; Purucker, Lennart

Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost Inproceedings

In: Proceedings of the Third International Conference on Automated Machine Learning (AutoML 2024), Workshop Track, 2024.

Bergman, Eddie; Purucker, Lennart; Hutter, Frank

Don’t Waste Your Time: Early Stopping Cross-Validation Inproceedings

In: Proceedings of the Third International Conference on Automated Machine Learning (AutoML 2024), Methods Track, 2024.

Wegmeth, Lukas; Vente, Tobias; Purucker, Lennart

Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems Inproceedings

In: European Conference on Information Retrieval, pp. 140–156, Springer 2024.

Bergman, Edward; Feurer, Matthias; Bahram, Aron; Balef, Amir Rezaei; Purucker, Lennart; Segel, Sarah; Lindauer, Marius; Hutter, Frank; Eggensperger, Katharina

AMLTK: A Modular AutoML Toolkit in Python Journal Article

In: Journal of Open Source Software, vol. 9, no. 100, pp. 6367, 2024.

2023

Wegmeth, Lukas; Vente, Tobias; Purucker, Lennart; Beel, Joeran

The Effect of Random Seeds for Data Splitting on Recommendation Accuracy Conference

Perspectives on the Evaluation of Recommender Systems Workshop (PERSPECTIVES 2023), co-located with the 17th ACM Conference on Recommender Systems, 2023.