Jake Robertson

I'm a PhD student at the ELLIS Institute Tübingen working at the legal, ethical, and technical intersections of AI and society. Currently working on algorithmic fairness, causal machine learning, and prior-data-fitted networks (PFNs). Check out my CV and work below!

Highlights

-- "Do-PFN: In-Context Learning for Causal Effect Estimation" accepted at ICML SIM and FMSD Workshops (June, 2025)

-- Gave a talk with Arik Reuter at the SmallData Workshop on Causal Modeling (May, 2025)

-- "FairPFN: A Tabular Foundation Model for Causal Fairness" accepted at ICML (May, 2025)

-- Started my PhD at the ELLIS Institute Tübingen (November, 2024)

-- Presented ManyFairHPO at the AI Ethics and Society Conference in San Jose (October, 2024)

-- Presented FairPFN at AutoML Conference in Paris (September, 2024)

-- Presented FairPFN at ICML Next Generation AI Safety Workshop in Vienna (July, 26, 2024)

-- Defended my Master's Thesis "FairPFN: Transformers Can do Counterfactual Fairness" (August, 2024)

-- Attended the AI For Social Good Seminar at Schloss Dagstuhl (February, 2024)

-- Presented first conference paper at the AI Ethics and Society Conference at Oxford (August, 2022)

-- Started my Master's at the University of Freiburg (October, 2021)

-- Presented first workshop paper at the Genetic and Evolutionary Computing Conference in Lille - online (July, 2012)

-- Received the NSERC Undergraduate Student Research Award (USRA) to work with Professor Ting Hu (May, 2019)

-- Received the Queen's University Principal's Scholarship for a 95% entrance average (September, 2017)

-- Started my Bachelor's at Queen's University (September, 2017)

Featured Work

PFNs & Causal Inference

ICML '25 SIM/FMSD

July 2025, Vancouver, Canada

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

ArXiv
PFNs & Causal Fairness

ICML'25

July 2025, Vancouver, Canada

"FairPFN: A Tabular Foundation Model for Causal Fairness"

ArXiv
Fairness-Aware AutoML

AIES'24

Aug 2024, San Jose, USA

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

ArXiv
Fairness & Interpretability

AIES'22

Aug 2022, Oxford, UK

"A Bio-Inspired Framework for Machine Bias Interpretation"

ACM Digital Library

Publications

Google Scholar

*Jake Robertson, *Arik Reuter, Noah Hollmann, Siyuan Guo, Frank Hutter, and Bernhard Schölkopf. 2025. Do-PFN: In-Context Learning for Causal Effect Estimation. In Proceedings of the 2025 ICML Scaling Intervention Models (SIM'25) and Foundation Models for Structured Data Workshops (FMSD'25), July 13-19, 2025, Vancouver, Canada. 4 pages

Jake Robertson, Noah Hollmann, Samuel Müller, Noor Awad, and Frank Hutter. 2025. FairPFN: A Tabular Foundation Model for Causal Fairness. In Proceedings of the 2025 International Conference on Machine Learning (ICML’25), July 13-19, 2025, Vancouver, Canada. 9 pages

Jake Robertson, Thorsten Schmidt, Frank Hutter, and Noor Awad. 2024. A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes. In Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society (AIES’24), October 21-23, 2024, San Jose, USA. 11 pages

Jake Robertson, Noah Hollmann, Noor Awad, and Frank Hutter. 2024. FairPFN: Transformers Can do Counterfactual Fairness. In Proceedings of the ICML Next Generation AI Safety Workshop 2024 (NextGenAISafety’24), July 26, 2024, Vienna, Austria. 2 pages

Jake Robertson, Catherine Stinson, and Ting Hu. 2022. A Bio-Inspired Framework for Machine Bias Interpretation. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES’22), August 1–3, 2022, Oxford, United Kingdom. 11 pages

Jake Robertson and Ting Hu. 2021. An Evolutionary Approach to Interpretable Learning. In Proceedings of the Genetic and Evolutionary Computation Conference 2021 (GECCO’21), July 10-14, 2021, Lille, France. 2 pages

About Me

I finished my Bachelor's in Computing at Queen's University in 2021, where I recieved the Principal's Scholarship and was granted the NSERC Undergraduate Research Award (USRA) to work with Professor Ting Hu. Recently, I completed my Master's in Computer Science (Artificial Intelligence) at the University of Freiburg, where I worked as a research assistant (HiWi) in the Freiburg Machine Learning Lab. The final year of my Master's degree was fully funded by the Konrad Zuse School of Excellence in Learning and Intelligent systems (ELIZA). I started my PhD in November 2024 at the ELLIS Institute Tübingen under the supervision of Professor Frank Hutter.

Contact Info

  1. ELLIS Institute Tübingen
  2. Maria-von-Linden-Straße 2
  3. Tübingen, BW, Germany
  4. Machine Learning Lab Freiburg
  5. Georges Köhler Allee 74
  6. Freiburg, BW, Germany
  7. Email _ GitHub _ LinkedIn