Samuel Müller

According to our database, Samuel Müller authored at least 20 papers between 2019 and 2025.

Collaborative distances:

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

2025

Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data.

Anurag Garg, Muhammad Ali, Noah Hollmann, Lennart Purucker, Samuel Müller, Frank Hutter

CoRR, July, 2025

FairPFN: A Tabular Foundation Model for Causal Fairness.

Jake Robertson, Noah Hollmann, Samuel Müller, Noor H. Awad, Frank Hutter

CoRR, June, 2025

Position: The Future of Bayesian Prediction Is Prior-Fitted.

Samuel Müller, Arik Reuter, Noah Hollmann, David Rügamer, Frank Hutter

CoRR, May, 2025

Accurate predictions on small data with a tabular foundation model.

Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, Frank Hutter

Nat., January, 2025

The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features.

Shi Bin Hoo, Samuel Müller, David Salinas, Frank Hutter

CoRR, January, 2025

2024

TabPFN.

Noah Hollmann, Frank Hutter, Samuel Gabriel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Robin Tibor Schirrmeister

Dataset, October, 2024

Bayes' Power for Explaining In-Context Learning Generalizations.

Samuel Müller, Noah Hollmann, Frank Hutter

CoRR, 2024

Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data.

Kai Helli, David Schnurr, Noah Hollmann, Samuel Müller, Frank Hutter

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

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