Byte-Pair Encoding for Text-to-SQL Generation. - Samuel Müller 005; Andreas Vlachos 001 (CoRR, 2019)

TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation. - Samuel G. Müller; Frank Hutter (ICCV, 2021)

In-Loop Meta-Learning with Gradient-Alignment Reward. - Samuel Müller 005; André Biedenkapp; Frank Hutter (CoRR, 2021)

Transformers Can Do Bayesian Inference. - Samuel Müller 005; Noah Hollmann; Sebastian Pineda-Arango; Josif Grabocka; Frank Hutter (ICLR, 2022)

Meta-Learning a Real-Time Tabular AutoML Method For Small Data. - Noah Hollmann; Samuel Müller 005; Katharina Eggensperger; Frank Hutter (CoRR, 2022)

On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning. - Diane Wagner; Fabio Ferreira; Danny Stoll; Robin Tibor Schirrmeister; Samuel Müller 005; Frank Hutter (CoRR, 2022)

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. - Noah Hollmann; Samuel Müller 005; Katharina Eggensperger; Frank Hutter (ICLR, 2023)

PFNs4BO: In-Context Learning for Bayesian Optimization. - Samuel Müller 005; Matthias Feurer; Noah Hollmann; Frank Hutter (ICML, 2023)

Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks. - Steven Adriaensen; Herilalaina Rakotoarison; Samuel Müller 005; Frank Hutter (NeurIPS, 2023)

Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering. - Noah Hollmann; Samuel Müller 005; Frank Hutter (NeurIPS, 2023)

LLMs for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering. - Noah Hollmann; Samuel Müller 005; Frank Hutter (CoRR, 2023)

PFNs Are Flexible Models for Real-World Bayesian Optimization. - Samuel Müller 005; Matthias Feurer; Noah Hollmann; Frank Hutter (CoRR, 2023)

Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data. - Kai Helli; David Schnurr; Noah Hollmann; Samuel Müller 005; Frank Hutter (NeurIPS, 2024)

Bayes' Power for Explaining In-Context Learning Generalizations. - Samuel Müller 005; Noah Hollmann; Frank Hutter (CoRR, 2024)

TabPFN. - Noah Hollmann; Frank Hutter; Samuel Gabriel Müller; Lennart Purucker; Arjun Krishnakumar; Max Körfer; Robin Tibor Schirrmeister (Zenodo, 2024)

The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features. - Shi Bin Hoo; Samuel Müller 005; David Salinas; Frank Hutter (CoRR, 2025)

Accurate predictions on small data with a tabular foundation model. - Noah Hollmann; Samuel Müller 005; Lennart Purucker; Arjun Krishnakumar; Max Körfer; Shi Bin Hoo; Robin Tibor Schirrmeister; Frank Hutter (Nat., 2025)

Position: The Future of Bayesian Prediction Is Prior-Fitted. - Samuel Müller 005; Arik Reuter; Noah Hollmann; David Rügamer; Frank Hutter (CoRR, 2025)

FairPFN: A Tabular Foundation Model for Causal Fairness. - Jake Robertson; Noah Hollmann; Samuel Müller 005; Noor H. Awad; Frank Hutter (CoRR, 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 005; Frank Hutter (CoRR, 2025)