van der Schaar Lab

van der Schaar Lab at NeurIPS 2020: 9 papers accepted

Note: a previous version of this post (published on September 27) listed a total of 8 papers as being accepted. This has been revised upward to 9 papers, as one “conditional” paper has now been officially accepted.

The van der Schaar Lab has set a new group record for representation at NeurIPS 2020—widely considered the world’s largest AI and machine learning research conference—with a total of 9 papers accepted for publication.

This is an unprecedented achievement for the lab, and demonstrates the diverse strengths of its small research team. The papers cover diverse topics, such as interpretability, uncertainty quantification, causal inference, and imitation learning. Applications in healthcare are similarly broad, ranging from treatment effect estimation to predicting the impact of COVID-19 spread prevention policies.

Titles, authors and abstracts for all 9 selected papers are given below.

When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes

Zhaozhi Qian, Ahmed Alaa, Mihaela van der Schaar

Abstract

Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification

Hyun-Suk Lee, Yao Zhang, William Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar

Abstract

VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain

Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar

Abstract

OrganITE: Optimal transplant donor organ offering using an individual treatment effect

Jeroen Berrevoets, James Jordon, Ioana Bica, Alexander Gimson, Mihaela van der Schaar

Abstract

CASTLE: Regularization via Auxiliary Causal Graph Discovery

Trent Kyono, Yao Zhang, Mihaela van der Schaar

Abstract

Learning outside the Black-Box: The pursuit of interpretable models

Jonathan Crabbe, Yao Zhang, William Zame, Mihaela van der Schaar

Abstract

Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks

Ioana Bica, James Jordon, Mihaela van der Schaar

Abstract

Gradient Regularized V-Learning for Dynamic Treatment Regimes

Yao Zhang, Mihaela van der Schaar

Abstract

Strictly Batch Imitation Learning by Energy-based Distribution Matching

Daniel Jarrett, Ioana Bica, 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.