An evolutionary approach to interpretable learning. - Jake Robertson; Ting Hu 001 (GECCO Companion, 2021)

A Bio-Inspired Framework for Machine Bias Interpretation. - Jake Robertson; Catherine Stinson; Ting Hu 001 (AIES, 2022)

A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes. - Jake Robertson; Thorsten Schmidt; Frank Hutter; Noor H. Awad (AIES (1), 2024)

FairPFN: Transformers Can do Counterfactual Fairness. - Jake Robertson; Noah Hollmann; Noor H. Awad; Frank Hutter (CoRR, 2024)

Do-PFN: In-Context Learning for Causal Effect Estimation. - Jake Robertson; Arik Reuter; Siyuan Guo; Noah Hollmann; Frank Hutter; Bernhard Schölkopf (CoRR, 2025)

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