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Jake Fawkes
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2020 – today
- 2024
- [j2]Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic:
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects. Trans. Mach. Learn. Res. 2024 (2024) - [c3]Omri Ben-Dov, Jake Fawkes, Samira Samadi, Amartya Sanyal:
The Role of Learning Algorithms in Collective Action. ICML 2024 - [i6]Omri Ben-Dov, Jake Fawkes, Samira Samadi, Amartya Sanyal:
The Role of Learning Algorithms in Collective Action. CoRR abs/2405.06582 (2024) - [i5]Jake Fawkes, Lucile Ter-Minassian, Desi Ivanova, Uri Shalit, Christopher C. Holmes:
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition. CoRR abs/2409.07215 (2024) - 2023
- [j1]Robert Grout, Rishab Gupta, Ruby Bryant, Mawada A. Elmahgoub, Yijie Li, Khushbakht Irfanullah, Rahul F. Patel, Jake Fawkes, Catherine Inness:
Predicting disease onset from electronic health records for population health management: a scalable and explainable Deep Learning approach. Frontiers Artif. Intell. 6 (2023) - [c2]Shahine Bouabid, Jake Fawkes, Dino Sejdinovic:
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. ICML 2023: 2885-2913 - [i4]Shahine Bouabid, Jake Fawkes, Dino Sejdinovic:
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. CoRR abs/2301.11214 (2023) - [i3]Jake Fawkes, Robin J. Evans:
Results on Counterfactual Invariance. CoRR abs/2307.08519 (2023) - 2022
- [c1]Jake Fawkes, Robin J. Evans, Dino Sejdinovic:
Selection, Ignorability and Challenges With Causal Fairness. CLeaR 2022: 275-289 - [i2]Jake Fawkes, Robin J. Evans, Dino Sejdinovic:
Selection, Ignorability and Challenges With Causal Fairness. CoRR abs/2202.13774 (2022) - [i1]Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic:
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap. CoRR abs/2212.04922 (2022)
Coauthor Index
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