Search Results for author: Zhaozhi Qian

Found 28 papers, 19 papers with code

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.

Computational Efficiency Low-Rank Matrix Completion

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.

counterfactual Gaussian Processes +1

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 +1

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.

BIG-bench Machine Learning

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.

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 counterfactual +2

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.

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.

regression Symbolic Regression +1

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

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 valid

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.

counterfactual

Neural Laplace: Learning diverse classes of differential equations in the Laplace domain

2 code implementations10 Jun 2022 Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar

Neural Ordinary Differential Equations model dynamical systems with ODEs learned by neural networks.

Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations

2 code implementations16 Jun 2022 Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar

To assess solutions to this problem, we propose a controllable simulation environment based on a model of tumor growth for a range of scenarios with irregular sampling reflective of a variety of clinical scenarios.

Causal Inference counterfactual +2

Navigating causal deep learning

no code implementations1 Dec 2022 Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar

With CDL, researchers aim to structure and encode causal knowledge in the extremely flexible representation space of deep learning models.

Synthcity: facilitating innovative use cases of synthetic data in different data modalities

2 code implementations18 Jan 2023 Zhaozhi Qian, Bogdan-Constantin Cebere, Mihaela van der Schaar

Synthcity is an open-source software package for innovative use cases of synthetic data in ML fairness, privacy and augmentation across diverse tabular data modalities, including static data, regular and irregular time series, data with censoring, multi-source data, composite data, and more.

Fairness Irregular Time Series +2

Membership Inference Attacks against Synthetic Data through Overfitting Detection

1 code implementation24 Feb 2023 Boris van Breugel, Hao Sun, Zhaozhi Qian, Mihaela van der Schaar

In this work we argue for a realistic MIA setting that assumes the attacker has some knowledge of the underlying data distribution.

Neural Laplace Control for Continuous-time Delayed Systems

2 code implementations24 Feb 2023 Samuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, Mihaela van der Schaar

Many real-world offline reinforcement learning (RL) problems involve continuous-time environments with delays.

Model Predictive Control Offline RL +1

Causal Deep Learning

no code implementations3 Mar 2023 Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar

Our framework clearly identifies which assumptions are testable and which ones are not, such that the resulting solutions can be judiciously adopted in practice.

Synthetic data, real errors: how (not) to publish and use synthetic data

1 code implementation16 May 2023 Boris van Breugel, Zhaozhi Qian, Mihaela van der Schaar

Generating synthetic data through generative models is gaining interest in the ML community and beyond, promising a future where datasets can be tailored to individual needs.

Uncertainty Quantification

Learning Representations without Compositional Assumptions

2 code implementations31 May 2023 Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar

This paper addresses unsupervised representation learning on tabular data containing multiple views generated by distinct sources of measurement.

Representation Learning valid

GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure

2 code implementations ICLR 2023 Tennison Liu, Zhaozhi Qian, Jeroen Berrevoets, Mihaela van der Schaar

Specifically, we introduce GOGGLE, an end-to-end message passing scheme that jointly learns the relational structure and corresponding functional relationships as the basis of generating synthetic samples.

Clairvoyance: A Pipeline Toolkit for Medical Time Series

1 code implementation 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

TRIAGE: Characterizing and auditing training data for improved regression

2 code implementations NeurIPS 2023 Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar

Data quality is crucial for robust machine learning algorithms, with the recent interest in data-centric AI emphasizing the importance of training data characterization.

regression

Deep Generative Symbolic Regression

2 code implementations30 Dec 2023 Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar

Symbolic regression (SR) aims to discover concise closed-form mathematical equations from data, a task fundamental to scientific discovery.

regression Symbolic Regression

Adaptive Experiment Design with Synthetic Controls

1 code implementation30 Jan 2024 Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar

Clinical trials are typically run in order to understand the effects of a new treatment on a given population of patients.

DAGnosis: Localized Identification of Data Inconsistencies using Structures

2 code implementations26 Feb 2024 Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar

Identification and appropriate handling of inconsistencies in data at deployment time is crucial to reliably use machine learning models.

ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference

2 code implementations16 Mar 2024 Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar

Above all, we consider the introduction of a completely new type of solution to be our most important contribution as it may spark entirely new innovations in treatment effects in general.

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