Search Results for author: Zhaozhi Qian

Found 13 papers, 5 papers with code

SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes

1 code implementation NeurIPS 2021 Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela Wood, Mihaela van der Schaar

Most of the medical observational studies estimate the causal treatment effects using electronic health records (EHR), where a patient's covariates and outcomes are both observed longitudinally.

Estimating Multi-cause Treatment Effects via Single-cause Perturbation

1 code implementation NeurIPS 2021 Zhaozhi Qian, Alicia Curth, Mihaela van der Schaar

Most existing methods for conditional average treatment effect estimation are designed to estimate the effect of a single cause - only one variable can be intervened on at one time.

Causal Inference

Explaining Latent Representations with a Corpus of Examples

1 code implementation NeurIPS 2021 Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar

SimplEx uses the corpus to improve the user's understanding of the latent space with post-hoc explanations answering two questions: (1) Which corpus examples explain the prediction issued for a given test example?

Image Classification Mortality Prediction

D-CODE: Discovering Closed-form ODEs from Observed Trajectories

no code implementations ICLR 2022 Zhaozhi Qian, Krzysztof Kacprzyk, Mihaela van der Schaar

In the experiments, D-CODE successfully discovered the governing equations of a diverse range of dynamical systems under challenging measurement settings with high noise and infrequent sampling.

Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression

no code implementations NeurIPS 2021 Zhaozhi Qian, William R. Zame, Lucas M. Fleuren, Paul Elbers, Mihaela van der Schaar

To close this gap, we propose the latent hybridisation model (LHM) that integrates a system of expert-designed ODEs with machine-learned Neural ODEs to fully describe the dynamics of the system and to link the expert and latent variables to observable quantities.

Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge

no code implementations11 Feb 2021 Trent Kyono, Ioana Bica, Zhaozhi Qian, Mihaela van der Schaar

We leverage the invariance of causal structures across domains to propose a novel model selection metric specifically designed for ITE methods under the UDA setting.

Causal Inference Model Selection +1

Clairvoyance: A Pipeline Toolkit for Medical Time Series

no code implementations ICLR 2021 Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar

Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support.

AutoML Time Series

SyncTwin: Transparent Treatment Effect Estimation under Temporal Confounding

no code implementations1 Jan 2021 Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela Wood, Mihaela van der Schaar

Estimating causal treatment effects using observational data is a problem with few solutions when the confounder has a temporal structure, e. g. the history of disease progression might impact both treatment decisions and clinical outcomes.

CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19

no code implementations27 Jul 2020 Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar

The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources.

Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift

no code implementations ICML 2020 Alex J. Chan, Ahmed M. Alaa, Zhaozhi Qian, Mihaela van der Schaar

In this paper, we develop an approximate Bayesian inference scheme based on posterior regularisation, wherein unlabelled target data are used as "pseudo-labels" of model confidence that are used to regularise the model's loss on labelled source data.

Bayesian Inference Decision Making

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

1 code implementation NeurIPS 2020 Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar

To this end, this paper develops a Bayesian model for predicting the effects of COVID-19 lockdown policies in a global context -- we treat each country as a distinct data point, and exploit variations of policies across countries to learn country-specific policy effects.

Gaussian Processes Variational Inference

Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes

no code implementations27 Jan 2017 Franz J. Király, Zhaozhi Qian

Prediction and modelling of competitive sports outcomes has received much recent attention, especially from the Bayesian statistics and machine learning communities.

Low-Rank Matrix Completion

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