van der Schaar Lab

van der Schaar Lab at ICML 2020: seven papers and a tutorial

Photo by bantersnaps on Unsplash

The van der Schaar Lab’s diverse and pioneering research will be on full display at the 2020 International Conference on Machine Learning (ICML). Seven papers have been accepted for publication at the event, and Mihaela van der Schaar will be giving a tutorial on machine learning for healthcare as well as two keynote presentations in two different workshops.

Having seven papers accepted for publication is a clear demonstration of the lab’s pre-eminent position among academic teams in the United Kingdom and Europe. Additionally, Mihaela is ranked among the top 10 academics by papers accepted for the conference, and is the top-ranked female academic.

As the premier gathering of professionals dedicated to the advancement of machine learning, ICML is renowned for presenting and publishing cutting-edge research. Participants span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers.

Mihaela’s tutorial at ICML will be on the topic of “Machine Learning for Healthcare: Challenges, Formalisms and Methods, and Research Frontiers” while her keynotes will be in workshops on AutoML and missing data imputation.

Each of the seven papers authored by the lab’s researchers presents a novel solution to an important problem, with a substantial potential impact on the application of machine learning in medicine. The papers cover diverse topics, including new techniques for providing uncertainty estimates, new methods for temporal phenotyping using deep predictive clustering, and new methods for finding the optimal doses for clinical trials with safety constraints.

Titles, authors and abstracts for all seven selected papers are given below.

Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions

Ahmed Alaa, Mihaela van der Schaar

Abstract

Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions

Ahmed Alaa, Mihaela van der Schaar

Abstract

Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift

Alex Chan, Ahmed Alaa, Zhaozhi Qian, Mihaela van der Schaar

Abstract

Temporal Phenotyping using Deep Predicting Clustering of Disease Progression

Changhee Lee, Mihaela van der Schaar

Abstract

Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints

Cong Shen, Sofia Villar, Zhiyang Wang, Mihaela van der Schaar

Abstract

Inverse Active Sensing: Modeling and Understanding Timely Decision-Making

Dan Jarrett, Mihaela van der Schaar

Abstract

Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders

Ioana Bica, Ahmed Alaa, Mihaela van der Schaar

Abstract

For a full list of the van der Schaar Lab’s publications, click here.

Nick Maxfield

From 2020 to 2022, Nick oversaw the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.