Samuel Müller

24 papers A* 7Journal 16Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Accurate predictions on small data with a tabular foundation model. Nat. Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, Frank Hutter
2025	A*	`conf`	FairPFN: A Tabular Foundation Model for Causal Fairness. ICML Jake Robertson, Noah Hollmann, Samuel Müller, Noor H. Awad, Frank Hutter
2025	J	`jnl`	FairPFN: A Tabular Foundation Model for Causal Fairness. CoRR Jake Robertson, Noah Hollmann, Samuel Müller, Noor H. Awad, Frank Hutter
2025	—	`conf`	Position: The Future of Bayesian Prediction Is Prior-Fitted. ICML (Position Papers) Samuel Müller, Arik Reuter, Noah Hollmann, David Rügamer, Frank Hutter
2025	J	`jnl`	Position: The Future of Bayesian Prediction Is Prior-Fitted. CoRR Samuel Müller, Arik Reuter, Noah Hollmann, David Rügamer, Frank Hutter
2025	J	`jnl`	Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data. CoRR Anurag Garg, Muhammad Ali, Noah Hollmann, Lennart Purucker, Samuel Müller, Frank Hutter
2025	J	`jnl`	The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features. CoRR Shi Bin Hoo, Samuel Müller, David Salinas, Frank Hutter
2025	J	`jnl`	Tune My Adam, Please! CoRR Theodoros Athanasiadis, Steven Adriaensen, Samuel Müller, Frank Hutter
2024	J	`jnl`	Bayes' Power for Explaining In-Context Learning Generalizations. CoRR Samuel Müller, Noah Hollmann, Frank Hutter
2024	A*	`conf`	Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data. NeurIPS Kai Helli, David Schnurr, Noah Hollmann, Samuel Müller, Frank Hutter
2024	J	`jnl`	Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data. CoRR Kai Helli, David Schnurr, Noah Hollmann, Samuel Müller, Frank Hutter
2023	A*	`conf`	Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks. NeurIPS Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Frank Hutter
2023	J	`jnl`	Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks. CoRR Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Frank Hutter
2023	J	`jnl`	LLMs for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering. CoRR Noah Hollmann, Samuel Müller, Frank Hutter
2023	A*	`conf`	Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering. NeurIPS Noah Hollmann, Samuel Müller, Frank Hutter
2023	J	`jnl`	PFNs Are Flexible Models for Real-World Bayesian Optimization. CoRR Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter
2023	A*	`conf`	PFNs4BO: In-Context Learning for Bayesian Optimization. ICML Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter
2023	A*	`conf`	TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. ICLR Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter
2022	J	`jnl`	Meta-Learning a Real-Time Tabular AutoML Method For Small Data. CoRR Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter
2022	J	`jnl`	On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning. CoRR Diane Wagner, Fabio Ferreira, Danny Stoll, Robin Tibor Schirrmeister, Samuel Müller, Frank Hutter
2022	A*	`conf`	Transformers Can Do Bayesian Inference. ICLR Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter
2021	J	`jnl`	In-Loop Meta-Learning with Gradient-Alignment Reward. CoRR Samuel Müller, André Biedenkapp, Frank Hutter
2021	J	`jnl`	Transformers Can Do Bayesian Inference. CoRR Samuel Müller, Noah Hollmann, Sebastian Pineda-Arango, Josif Grabocka, Frank Hutter
2019	J	`jnl`	Byte-Pair Encoding for Text-to-SQL Generation. CoRR Samuel Müller, Andreas Vlachos