44 citations · 147 across the 21 of their papers we have counts for
21 papers
NISPO: Open-source IUPAC name generation tool
Nicholas T. Runcie, Fergus Imrie, Charlotte M. Deane
Systematic International Union of Pure and Applied Chemistry (IUPAC) names are standard for communicating molecular structures in chemical literature, patents, and databases. We in…
Molecular Representations for Large Language Models
Nicholas T. Runcie, Fergus Imrie, Charlotte M. Deane
Large Language Models (LLMs) are increasingly being used to support scientific discovery. In chemistry, tasks such as reaction prediction and structure elucidation require reasonin…
Stochastic Encodings for Active Feature Acquisition
Alexander Norcliffe, Changhee Lee, Fergus Imrie +2
Active Feature Acquisition is an instance-wise, sequential decision making problem. The aim is to dynamically select which feature to measure based on current observations, indepen…
Assessing the Chemical Intelligence of Large Language Models
Nicholas T. Runcie, Charlotte M. Deane, Fergus Imrie
Large Language Models are versatile, general-purpose tools with a wide range of applications. Recently, the advent of "reasoning models" has led to substantial improvements in thei…
Automated Ensemble Multimodal Machine Learning for Healthcare
Fergus Imrie, Stefan Denner, Lucas S. Brunschwig +2
The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approac…
You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling
Nabeel Seedat, Nicolas Huynh, Fergus Imrie +1
Pseudo-labeling is a popular semi-supervised learning technique to leverage unlabeled data when labeled samples are scarce. The generation and selection of pseudo-labels heavily re…
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI
Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
Characterizing samples that are difficult to learn from is crucial to developing highly performant ML models. This has led to numerous Hardness Characterization Methods (HCMs) that…
A Neural Framework for Generalized Causal Sensitivity Analysis
Dennis Frauen, Fergus Imrie, Alicia Curth +3
Unobserved confounding is common in many applications, making causal inference from observational data challenging. As a remedy, causal sensitivity analysis is an important tool to…
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data
Boris van Breugel, Nabeel Seedat, Fergus Imrie +1
Evaluating the performance of machine learning models on diverse and underrepresented subgroups is essential for ensuring fairness and reliability in real-world applications. Howev…
Redefining Digital Health Interfaces with Large Language Models
Fergus Imrie, Paulius Rauba, Mihaela van der Schaar
Digital health tools have the potential to significantly improve the delivery of healthcare services. However, their adoption remains comparatively limited due, in part, to challen…
Machine Learning with Requirements: a Manifesto
Eleonora Giunchiglia, Fergus Imrie, Mihaela van der Schaar +1
In the recent years, machine learning has made great advancements that have been at the root of many breakthroughs in different application domains. However, it is still an open is…
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
Alan Jeffares, Tennison Liu, Jonathan Crabbé +2
Despite their success with unstructured data, deep neural networks are not yet a panacea for structured tabular data. In the tabular domain, their efficiency crucially relies on va…
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
Alexander Norcliffe, Bogdan Cebere, Fergus Imrie +2
Synthetic data is becoming an increasingly promising technology, and successful applications can improve privacy, fairness, and data democratization. While there are many methods f…
Improving Adaptive Conformal Prediction Using Self-Supervised Learning
Nabeel Seedat, Alan Jeffares, Fergus Imrie +1
Conformal prediction is a powerful distribution-free tool for uncertainty quantification, establishing valid prediction intervals with finite-sample guarantees. To produce valid in…
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems
Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
While there have been a number of remarkable breakthroughs in machine learning (ML), much of the focus has been placed on model development. However, to truly realize the potential…
Composite Feature Selection using Deep Ensembles
Fergus Imrie, Alexander Norcliffe, Pietro Lio +1
In many real world problems, features do not act alone but in combination with each other. For example, in genomics, diseases might not be caused by any single mutation but require…
AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning
Fergus Imrie, Bogdan Cebere, Eoin F. McKinney +1
Diagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conven…
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
Nabeel Seedat, Fergus Imrie, Alexis Bellot +2
Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare by assisting decision-makers to answer ''what-iF'' questions. Existing causal infer…
Differentiable and Transportable Structure Learning
Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie +1
Directed acyclic graphs (DAGs) encode a lot of information about a particular distribution in their structure. However, compute required to infer these structures is typically supe…
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
Jeroen Berrevoets, Fergus Imrie, Trent Kyono +2
Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects. This makes treatment effect estimation from data with missin…
Explaining Latent Representations with a Corpus of Examples
Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie +1
Modern machine learning models are complicated. Most of them rely on convoluted latent representations of their input to issue a prediction. To achieve greater transparency than a…