599 citations · 1.6k across the 142 of their papers we have counts for
19 papers · 1 filter
Learning outside the Black-Box: The pursuit of interpretable models
Jonathan Crabbé, Yao Zhang, William Zame +1
Machine Learning has proved its ability to produce accurate models but the deployment of these models outside the machine learning community has been hindered by the difficulties o…
CASTLE: Regularization via Auxiliary Causal Graph Discovery
Trent Kyono, Yao Zhang, Mihaela van der Schaar
Regularization improves generalization of supervised models to out-of-sample data. Prior works have shown that prediction in the causal direction (effect from cause) results in low…
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…
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Ahmed M. Alaa, Mihaela van der Schaar
Deep learning models achieve high predictive accuracy across a broad spectrum of tasks, but rigorously quantifying their predictive uncertainty remains challenging. Usable estimate…
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
Ahmed M. Alaa, Mihaela van der Schaar
Recurrent neural networks (RNNs) are instrumental in modelling sequential and time-series data. Yet, when using RNNs to inform decision-making, predictions by themselves are not su…
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Alex J. Chan, Ahmed M. Alaa, Zhaozhi Qian +1
Modern neural networks have proven to be powerful function approximators, providing state-of-the-art performance in a multitude of applications. They however fall short in their ab…
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…
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
Changhee Lee, Mihaela van der Schaar
Due to the wider availability of modern electronic health records, patient care data is often being stored in the form of time-series. Clustering such time-series data is crucial f…
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Cong Shen, Zhiyang Wang, Sofia S. Villar +1
Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex. Despite t…
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar
The coronavirus disease 2019 (COVID-19) global pandemic has led many countries to impose unprecedented lockdown measures in order to slow down the outbreak. Questions on whether go…
Strictly Batch Imitation Learning by Energy-based Distribution Matching
Daniel Jarrett, Ioana Bica, Mihaela van der Schaar
Consider learning a policy purely on the basis of demonstrated behavior -- that is, with no access to reinforcement signals, no knowledge of transition dynamics, and no further int…
AutoCP: Automated Pipelines for Accurate Prediction Intervals
Yao Zhang, William Zame, Mihaela van der Schaar
Successful application of machine learning models to real-world prediction problems, e.g. financial forecasting and personalized medicine, has proved to be challenging, because suc…
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Hyun-Suk Lee, Yao Zhang, William Zame +3
Subgroup analysis of treatment effects plays an important role in applications from medicine to public policy to recommender systems. It allows physicians (for example) to identify…
A primer on coupled state-switching models for multiple interacting time series
Jennifer Pohle, Roland Langrock, Mihaela van der Schaar +2
State-switching models such as hidden Markov models or Markov-switching regression models are routinely applied to analyse sequences of observations that are driven by underlying n…
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…
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations
Ioana Bica, Ahmed M. Alaa, James Jordon +1
Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions. In this paper, w…
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks
Ioana Bica, James Jordon, Mihaela van der Schaar
While much attention has been given to the problem of estimating the effect of discrete interventions from observational data, relatively little work has been done in the setting o…
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…
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Zhaozhi Qian, Ahmed M. Alaa, Alexis Bellot +2
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has,…