| TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation SG Müller, F Hutter Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 329 | 2021 |
| TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second N Hollmann, S Müller, K Eggensperger, F Hutter The Eleventh International Conference on Learning Representations (ICLR), 2023 | 280 | 2023 |
| Transformers Can Do Bayesian Inference S Müller, N Hollmann, SP Arango, J Grabocka, F Hutter The Tenth International Conference on Learning Representations (ICLR), 2022 | 153 | 2022 |
| Large language models for automated data science: Introducing caafe for context-aware automated feature engineering N Hollmann, S Müller, F Hutter Advances in Neural Information Processing Systems 36, 2024 | 50 | 2024 |
| PFNs4BO: In-Context Learning for Bayesian Optimization S Müller, M Feurer, N Hollmann, F Hutter ICML 2023, 2023 | 32 | 2023 |
| Efficient bayesian learning curve extrapolation using prior-data fitted networks S Adriaensen, H Rakotoarison, S Müller, F Hutter Advances in Neural Information Processing Systems 36, 2024 | 27 | 2024 |
| On the importance of hyperparameters and data augmentation for self-supervised learning D Wagner, F Ferreira, D Stoll, RT Schirrmeister, S Müller, F Hutter arXiv preprint arXiv:2207.07875, 2022 | 20 | 2022 |
| Gpt for semi-automated data science: Introducing caafe for context-aware automated feature engineering N Hollmann, S Müller, F Hutter arXiv preprint arXiv:2305.03403, 2023 | 13 | 2023 |
| Tabpfn: A transformer that solves small tabular classification problems in a second. arXiv 2022 N Hollmann, S Müller, K Eggensperger, F Hutter arXiv preprint arXiv:2207.01848, 0 | 8 | |
| TabPFN: a transformer that solves small tabular classification problems in a second. arXiv N Hollmann, S Müller, K Eggensperger, F Hutter | 7 | 2023 |
| Accurate predictions on small data with a tabular foundation model N Hollmann, S Müller, L Purucker, A Krishnakumar, M Körfer, SB Hoo, ... Nature 637 (8045), 319-326, 2025 | 2 | 2025 |
| Simulation-Based Comparison of Novel Automated Construction Systems L Herrmann, R Boumann, M Lehmann, S Müller, T Bruckmann Robotics 11 (6), 119, 2022 | 2 | 2022 |
| In-loop meta-learning with gradient-alignment reward S Müller, A Biedenkapp, F Hutter arXiv preprint arXiv:2102.03275, 2021 | 2 | 2021 |
| The tabular foundation model TabPFN outperforms specialized time series forecasting models based on simple features SB Hoo, S Müller, D Salinas, F Hutter arXiv preprint arXiv:2501.02945, 2025 | 1 | 2025 |
| Drift-resilient tabPFN: In-context learning temporal distribution shifts on tabular data K Helli, D Schnurr, N Hollmann, S Müller, F Hutter arXiv preprint arXiv:2411.10634, 2024 | 1 | 2024 |
| Bayes' Power for Explaining In-Context Learning Generalizations S Müller, N Hollmann, F Hutter arXiv preprint arXiv:2410.01565, 2024 | | 2024 |
| Method and control device for generating training data for training a machine learning algorithm F Hutter, SG Mueller US Patent App. 17/657,396, 2022 | | 2022 |
| Training of machine learning systems for image processing SG Mueller, A Biedenkapp, F Hutter US Patent App. 17/573,723, 2022 | | 2022 |
| Byte-Pair Encoding for Text-to-SQL Generation S Müller, A Vlachos arXiv preprint arXiv:1910.08962, 2019 | | 2019 |
| Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data D Schnurr, K Helli, N Hollmann, S Müller, F Hutter NeurIPS 2024 Third Table Representation Learning Workshop, 0 | | |
Frank HutterELLIS Institute Tübingen & Professor, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de