Jake Fawkes

According to our database, Jake Fawkes authored at least 14 papers between 2022 and 2026.

Collaborative distances:

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

2026

f-Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data.

Jake Fawkes, Jason Hartford

CoRR, May, 2026

Towards Autonomous Mechanistic Reasoning in Virtual Cells.

Yunhui Jang, Lu Zhu, Jake Fawkes, Alisandra Kaye Denton, Dominique Beaini, Emmanuel Noutahi

CoRR, April, 2026

Observationally Informed Adaptive Causal Experimental Design.

Erdun Gao, Liang Zhang, Jake Fawkes, Aoqi Zuo, Wenqin Liu, Haoxuan Li, Mingming Gong, Dino Sejdinovic

CoRR, March, 2026

2025

Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation.

Erdun Gao, Jake Fawkes, Dino Sejdinovic

CoRR, September, 2025

Is Merging Worth It? Securely Evaluating the Information Gain for Causal Dataset Acquisition.

Jake Fawkes, Lucile Ter-Minassian, Desi R. Ivanova, Uri Shalit, Christopher C. Holmes

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

The Hardness of Validating Observational Studies with Experimental Data.

Jake Fawkes, Michael O'Riordan, Athanasios Vlontzos, Oriol Corcoll, Ciarán Mark Gilligan-Lee

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects.

Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic

Trans. Mach. Learn. Res., 2024

The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning.

Jake Fawkes, Nic Fishman, Mel Andrews, Zachary C. Lipton

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

The Role of Learning Algorithms in Collective Action.

Omri Ben-Dov, Jake Fawkes, Samira Samadi, Amartya Sanyal

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Predicting disease onset from electronic health records for population health management: a scalable and explainable Deep Learning approach.

Robert Grout, Rishab Gupta, Ruby Bryant, Mawada A. Elmahgoub, Yijie Li, Khushbakht Irfanullah, Rahul F. Patel, Jake Fawkes, Catherine Inness

Frontiers Artif. Intell., February, 2023

Results on Counterfactual Invariance.

Jake Fawkes, Robin J. Evans

CoRR, 2023

Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge.

Shahine Bouabid, Jake Fawkes, Dino Sejdinovic

Proceedings of the International Conference on Machine Learning, 2023

2022

Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap.

Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic

CoRR, 2022

Selection, Ignorability and Challenges With Causal Fairness.

Jake Fawkes, Robin J. Evans, Dino Sejdinovic

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022