304 citations · 1.6k across the 183 of their papers we have counts for
8 papers · 2 filters
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…
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain
Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar
Neural Ordinary Differential Equations model dynamical systems with ODEs learned by neural networks. However, ODEs are fundamentally inadequate to model systems with long-range dep…
D-CIPHER: Discovery of Closed-form Partial Differential Equations
Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar
Closed-form differential equations, including partial differential equations and higher-order ordinary differential equations, are one of the most important tools used by scientist…
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…
Inferring Lexicographically-Ordered Rewards from Preferences
Alihan Hüyük, William R. Zame, Mihaela van der Schaar
Modeling the preferences of agents over a set of alternatives is a principal concern in many areas. The dominant approach has been to find a single reward/utility function with the…