Samuel Müller

According to our database, Samuel Müller authored at least 12 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

PFNs Are Flexible Models for Real-World Bayesian Optimization.

Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter

CoRR, 2023

LLMs for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering.

Noah Hollmann, Samuel Müller, Frank Hutter

CoRR, 2023

Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering.

Noah Hollmann, Samuel Müller, Frank Hutter

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks.

Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Frank Hutter

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

PFNs4BO: In-Context Learning for Bayesian Optimization.

Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter

Proceedings of the International Conference on Machine Learning, 2023

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second.

Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning.

Diane Wagner, Fabio Ferreira, Danny Stoll, Robin Tibor Schirrmeister, Samuel Müller, Frank Hutter

CoRR, 2022

Meta-Learning a Real-Time Tabular AutoML Method For Small Data.

Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter

CoRR, 2022

Transformers Can Do Bayesian Inference.

Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

In-Loop Meta-Learning with Gradient-Alignment Reward.

Samuel Müller, André Biedenkapp, Frank Hutter

CoRR, 2021

TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation.

Samuel G. Müller, Frank Hutter

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2019

Byte-Pair Encoding for Text-to-SQL Generation.

Samuel Müller, Andreas Vlachos

CoRR, 2019