most citedHide-and-Seek Privacy Challenge

5 citations · 17 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG20205 cited

Hide-and-Seek Privacy Challenge

James Jordon, Daniel Jarrett, Jinsung Yoon +7

The clinical time-series setting poses a unique combination of challenges to data modeling and sharing. Due to the high dimensionality of clinical time series, adequate de-identifi…

stat.ML20205 cited

Inverse Active Sensing: Modeling and Understanding Timely Decision-Making

Daniel Jarrett, Mihaela van der Schaar

Evidence-based decision-making entails collecting (costly) observations about an underlying phenomenon of interest, and subsequently committing to an (informed) decision on the bas…

cs.LG20203 cited

Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning

Yao Zhang, Daniel Jarrett, Mihaela van der Schaar

An essential problem in automated machine learning (AutoML) is that of model selection. A unique challenge in the sequential setting is the fact that the optimal model itself may v…

stat.ML20204 cited

Target-Embedding Autoencoders for Supervised Representation Learning

Daniel Jarrett, Mihaela van der Schaar

Autoencoder-based learning has emerged as a staple for disciplining representations in unsupervised and semi-supervised settings. This paper analyzes a framework for improving gene…

cs.LG2018

MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks

Daniel Jarrett, Jinsung Yoon, Mihaela van der Schaar

Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often const…