Jake Robertson

According to our database, Jake Robertson authored at least 10 papers between 2021 and 2026.

Collaborative distances:

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

2026

TabPFN-3: Technical Report.

Léo Grinsztajn, Klemens Flöge, Oscar Key, Felix Birkel, Philipp Jund, Brendan Roof, Mihir Manium, Shi Bin Hoo, Magnus Bühler, Anurag Garg, Dominik Safaric, Jake Robertson, Benjamin Jäger, Simone Alessi, Adrian Hayler, Vladyslav Moroshan, Lennart Purucker, Philipp Singer, Alan Arazi, Julien Siems, Jan Hendrik Metzen, Georg Grab, Nick Erickson, Siyuan Guo, Eliott Kalfon, Simon Bing, David Salinas, Clara Cornu, Lilly Charlotte Wehrhahn, Diana Kriuchkova, Kursat Kaya, Lydia Sidhoum, Marie Salmon, Jerry Chen, Madelon Hulsebos, Yann LeCun, Samuel Müller, Bernhard Schölkopf, Sauraj Gambhir, Noah Hollmann, Frank Hutter

CoRR, May, 2026

Use What You Know: Causal Foundation Models with Partial Graphs.

Arik Reuter, Anish Dhir, Cristiana Diaconu, Jake Robertson, Ole Ossen, Frank Hutter, Adrian Weller, Mark van der Wilk, Bernhard Schölkopf

CoRR, February, 2026

2025

TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models.

Léo Grinsztajn, Klemens Flöge, Oscar Key, Felix Birkel, Philipp Jund, Brendan Roof, Benjamin Jäger, Dominik Safaric, Simone Alessi, Adrian Hayler, Mihir Manium, Rosen Yu, Felix Jablonski, Shi Bin Hoo, Anurag Garg, Jake Robertson, Magnus Bühler, Vladyslav Moroshan, Lennart Purucker, Clara Cornu, Lilly Charlotte Wehrhahn, Alessandro Bonetto, Bernhard Schölkopf, Sauraj Gambhir, Noah Hollmann, Frank Hutter

CoRR, November, 2025

Does TabPFN Understand Causal Structures?

Omar Swelam, Lennart Purucker, Jake Robertson, Hanne Raum, Joschka Boedecker, Frank Hutter

CoRR, November, 2025

Do-PFN: In-Context Learning for Causal Effect Estimation.

Jake Robertson, Arik Reuter, Siyuan Guo, Noah Hollmann, Frank Hutter, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

FairPFN: A Tabular Foundation Model for Causal Fairness.

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

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

FairPFN: Transformers Can do Counterfactual Fairness.

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

CoRR, 2024

A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes.

Jake Robertson, Thorsten Schmidt, Frank Hutter, Noor H. Awad

Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society (AIES-24) - Full Archival Papers, October 21-23, 2024, San Jose, California, USA, 2024

2022

A Bio-Inspired Framework for Machine Bias Interpretation.

Jake Robertson, Catherine Stinson, Ting Hu

Proceedings of the AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19, 2022

2021

An evolutionary approach to interpretable learning.

Jake Robertson, Ting Hu

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021