Fergus Imrie

According to our database, Fergus Imrie authored at least 26 papers between 2018 and 2024.

Collaborative distances:

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

2024

Navigating Data-Centric Artificial Intelligence With DC-Check: Advances, Challenges, and Opportunities.

Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

IEEE Trans. Artif. Intell., June, 2024

Automated Ensemble Multimodal Machine Learning for Healthcare.

Fergus Imrie, Stefan Denner, Lucas S. Brunschwig, Klaus H. Maier-Hein, Mihaela van der Schaar

CoRR, 2024

You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling.

Nabeel Seedat, Nicolas Huynh, Fergus Imrie, Mihaela van der Schaar

CoRR, 2024

Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI.

Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

Proceedings of the Twelfth International Conference on Learning Representations, 2024

A Neural Framework for Generalized Causal Sensitivity Analysis.

Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare.

Fergus Imrie, Robert Davis, Mihaela van der Schaar

Nat. Mac. Intell., August, 2023

Testing the limits of SMILES-based de novo molecular generation with curriculum and deep reinforcement learning.

Maranga Mokaya, Fergus Imrie, Willem P. van Hoorn, Aleksandra Kalisz, Anthony R. Bradley, Charlotte M. Deane

Nat. Mac. Intell., April, 2023

Redefining Digital Health Interfaces with Large Language Models.

Fergus Imrie, Paulius Rauba, Mihaela van der Schaar

CoRR, 2023

Machine Learning with Requirements: a Manifesto.

Eleonora Giunchiglia, Fergus Imrie, Mihaela van der Schaar, Thomas Lukasiewicz

CoRR, 2023

Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data.

Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Differentiable and Transportable Structure Learning.

Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

Proceedings of the International Conference on Machine Learning, 2023

TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization.

Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Improving Adaptive Conformal Prediction Using Self-Supervised Learning.

Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

SurvivalGAN: Generating Time-to-Event Data for Survival Analysis.

Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Liò, Mihaela van der Schaar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

To Impute or not to Impute? Missing Data in Treatment Effect Estimation.

Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration.

Thomas E. Hadfield, Fergus Imrie, Andy Merritt, Kristian Birchall, Charlotte M. Deane

J. Chem. Inf. Model., 2022

DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems.

Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

CoRR, 2022

AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning.

Fergus Imrie, Bogdan Cebere, Eoin F. McKinney, Mihaela van der Schaar

CoRR, 2022

Composite Feature Selection Using Deep Ensembles.

Fergus Imrie, Alexander Norcliffe, Pietro Lió, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations.

Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar

Proceedings of the International Conference on Machine Learning, 2022

Self-Supervision Enhanced Feature Selection with Correlated Gates.

Changhee Lee, Fergus Imrie, Mihaela van der Schaar

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Generating property-matched decoy molecules using deep learning.

Fergus Imrie, Anthony R. Bradley, Charlotte M. Deane

Bioinform., 2021

Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation.

Yuchao Qin, Fergus Imrie, Alihan Hüyük, Daniel Jarrett, Alexander Gimson, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Explaining Latent Representations with a Corpus of Examples.

Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Deep Generative Models for 3D Linker Design.

Fergus Imrie, Anthony R. Bradley, Mihaela van der Schaar, Charlotte M. Deane

J. Chem. Inf. Model., 2020

2018

Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data.

Fergus Imrie, Anthony R. Bradley, Mihaela van der Schaar, Charlotte M. Deane

J. Chem. Inf. Model., 2018