599 citations · 1.6k across the 142 of their papers we have counts for
13 papers · 2 filters
A Foundational Framework and Methodology for Personalized Early and Timely Diagnosis
Tim Schubert, Richard W Peck, Alexander Gimson +2
Early diagnosis of diseases holds the potential for deep transformation in healthcare by enabling better treatment options, improving long-term survival and quality of life, and re…
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces
Max Zhu, Katarzyna Kobalczyk, Andrija Petrovic +4
Despite the prevalence of tabular datasets, few-shot learning remains under-explored within this domain. Existing few-shot methods are not directly applicable to tabular datasets d…
Optimising Human-AI Collaboration by Learning Convincing Explanations
Alex J. Chan, Alihan Huyuk, Mihaela van der Schaar
Machine learning models are being increasingly deployed to take, or assist in taking, complicated and high-impact decisions, from quasi-autonomous vehicles to clinical decision sup…
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…
When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective
Hao Sun, Alex J. Chan, Nabeel Seedat +2
Evaluating the value of a hypothetical target policy with only a logged dataset is important but challenging. On the one hand, it brings opportunities for safe policy improvement u…
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Luis Oala, Manil Maskey, Lilith Bat-Leah +35
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure developm…
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark
Lasse Hansen, Nabeel Seedat, Mihaela van der Schaar +1
Synthetic data serves as an alternative in training machine learning models, particularly when real-world data is limited or inaccessible. However, ensuring that synthetic data mir…
Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models
Aleksa Bisercic, Mladen Nikolic, Mihaela van der Schaar +3
Tabular data is often hidden in text, particularly in medical diagnostic reports. Traditional machine learning (ML) models designed to work with tabular data, cannot effectively pr…
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
Jonathan Crabbé, Mihaela van der Schaar
Interpretability methods are valuable only if their explanations faithfully describe the explained model. In this work, we consider neural networks whose predictions are invariant…
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…
Safe AI for health and beyond -- Monitoring to transform a health service
Mahed Abroshan, Michael Burkhart, Oscar Giles +7
Machine learning techniques are effective for building predictive models because they identify patterns in large datasets. Development of a model for complex real-life problems oft…
Joint Training of Deep Ensembles Fails Due to Learner Collusion
Alan Jeffares, Tennison Liu, Jonathan Crabbé +1
Ensembles of machine learning models have been well established as a powerful method of improving performance over a single model. Traditionally, ensembling algorithms train their…