1 code implementation • 7 Mar 2024 • Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
Additionally, we propose the Hardness Characterization Analysis Toolkit (H-CAT), which supports comprehensive and quantitative benchmarking of HCMs across the hardness taxonomy and can easily be extended to new HCMs, hardness types, and datasets.
1 code implementation • 27 Nov 2023 • Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
Unobserved confounding is common in many applications, making causal inference from observational data challenging.
1 code implementation • 5 Oct 2023 • Fergus Imrie, Paulius Rauba, Mihaela van der Schaar
We develop a new prognostic tool using automated machine learning and demonstrate how LLMs can provide a unique interface to both our model and existing risk scores, highlighting the benefit compared to traditional interfaces for digital tools.
no code implementations • 7 Apr 2023 • Eleonora Giunchiglia, Fergus Imrie, Mihaela van der Schaar, Thomas Lukasiewicz
In the recent years, machine learning has made great advancements that have been at the root of many breakthroughs in different application domains.
2 code implementations • 9 Mar 2023 • Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar
In this work, we introduce Tabular Neural Gradient Orthogonalization and Specialization (TANGOS), a novel framework for regularization in the tabular setting built on latent unit attributions.
1 code implementation • 24 Feb 2023 • Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Lio, Mihaela van der Schaar
SurvivalGAN outperforms multiple baselines at generating survival data, and in particular addresses the failure modes as measured by the new metrics, in addition to improving downstream performance of survival models trained on the synthetic data.
2 code implementations • 23 Feb 2023 • Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar
However, the use of self-supervision beyond model pretraining and representation learning has been largely unexplored.
no code implementations • 9 Nov 2022 • Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
However, this remains a nascent area with no standardized framework to guide practitioners to the necessary data-centric considerations or to communicate the design of data-centric driven ML systems.
2 code implementations • 1 Nov 2022 • Fergus Imrie, Alexander Norcliffe, Pietro Lio, Mihaela van der Schaar
To do so, we define predictive groups in terms of linear and non-linear interactions between features.
1 code implementation • 21 Oct 2022 • Fergus Imrie, Bogdan Cebere, Eoin F. McKinney, Mihaela van der Schaar
However, the use of machine learning introduces a number of technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings.
2 code implementations • 16 Jun 2022 • Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar
To assess solutions to this problem, we propose a controllable simulation environment based on a model of tumor growth for a range of scenarios with irregular sampling reflective of a variety of clinical scenarios.
1 code implementation • 13 Jun 2022 • Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
Directed acyclic graphs (DAGs) encode a lot of information about a particular distribution in their structure.
1 code implementation • 4 Feb 2022 • Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar
However, no imputation at all also leads to biased estimates, as missingness determined by treatment introduces bias in covariates.
no code implementations • NeurIPS 2021 • Yuchao Qin, Fergus Imrie, Alihan Hüyük, Daniel Jarrett, alexander gimson, Mihaela van der Schaar
Significant effort has been placed on developing decision support tools to improve patient care.
1 code implementation • NeurIPS 2021 • Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar
SimplEx uses the corpus to improve the user's understanding of the latent space with post-hoc explanations answering two questions: (1) Which corpus examples explain the prediction issued for a given test example?
no code implementations • ICLR 2022 • Changhee Lee, Fergus Imrie, Mihaela van der Schaar
Discovering relevant input features for predicting a target variable is a key scientific question.