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ORCIDSamuel Müller 0005
Person information
- affiliation: University of Freiburg, Department of Computer Science, Germany
- affiliation (former): University of Cambridge, UK
Other persons with the same name
- Samuel Müller 0001
![[0000-0002-3087-8127]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-3087-8127")
— University of Sydney, School of Mathematics and Statistics, Australia - Samuel Müller 0002 — ETH Zurich, Switzerland (and 1 more)
- Samuel Müller 0003 — University of Hanover, Institute of Mechatronic Systems, Hannover, Germany
- Samuel Müller 0004 — University of Duisburg-Essen, Chair of Mechatronics, Duisburg, Germany
- Samuel Müller 0006 — Atlas Medical Technologies GmbH, Bochum, Germany
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2020 – today
- 2023
[c6]Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter:
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. ICLR 2023- [c5]Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter:
PFNs4BO: In-Context Learning for Bayesian Optimization. ICML 2023: 25444-25470 - [c4]Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Frank Hutter:
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks. NeurIPS 2023 - [c3]Noah Hollmann, Samuel Müller, Frank Hutter:
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering. NeurIPS 2023 - [i9]Noah Hollmann, Samuel Müller, Frank Hutter:
LLMs for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering. CoRR abs/2305.03403 (2023) - [i8]Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter:
PFNs Are Flexible Models for Real-World Bayesian Optimization. CoRR abs/2305.17535 (2023) - [i7]Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Frank Hutter:
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks. CoRR abs/2310.20447 (2023) - 2022
- [c2]Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter:
Transformers Can Do Bayesian Inference. ICLR 2022 - [i6]Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter:
Meta-Learning a Real-Time Tabular AutoML Method For Small Data. CoRR abs/2207.01848 (2022) - [i5]Diane Wagner, Fabio Ferreira, Danny Stoll, Robin Tibor Schirrmeister, Samuel Müller, Frank Hutter:
On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning. CoRR abs/2207.07875 (2022) - 2021
- [c1]Samuel G. Müller, Frank Hutter:
TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation. ICCV 2021: 754-762 - [i4]Samuel Müller, André Biedenkapp, Frank Hutter:
In-Loop Meta-Learning with Gradient-Alignment Reward. CoRR abs/2102.03275 (2021) - [i3]Samuel G. Müller, Frank Hutter:
TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation. CoRR abs/2103.10158 (2021) - [i2]Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter:
Transformers Can Do Bayesian Inference. CoRR abs/2112.10510 (2021)
2010 – 2019
- 2019
- [i1]Samuel Müller, Andreas Vlachos:
Byte-Pair Encoding for Text-to-SQL Generation. CoRR abs/1910.08962 (2019)
Coauthor Index
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