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Zhaozhi Qian

27 accepted papers

2025

Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference

Harry Amad, Zhaozhi Qian, Dennis Frauen, Julianna Piskorz, Stefan Feuerriegel, Mihaela van der Schaar
NeurIPS 2025poster

Causal inference is essential for developing and evaluating medical interventions, yet real-world medical datasets are often difficult to access due to regulatory barriers. This makes synthetic data a potentially valuable asset that enables these medical analyses, along with the development of new i…

Cited by 0Source ↗Scholar ↗
2024

Adaptive Experiment Design with Synthetic Controls

Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar
AISTATS 2024poster

Clinical trials are typically run in order to understand the effects of a new treatment on a given population of patients. However, patients in large populations rarely respond the same way to the same treatment. This heterogeneity in patient responses necessitates trials that investigate effects on…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

DAGnosis: Localized Identification of Data Inconsistencies using Structures

Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar
AISTATS 2024poster

Identification and appropriate handling of inconsistencies in data at deployment time is crucial to reliably use machine learning models. While recent data-centric methods are able to identify such inconsistencies with respect to the training set, they suffer from two key limitations: (1) suboptimal…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language Models

Samuel Holt, Zhaozhi Qian, Tennison Liu, Jim Weatherall, Mihaela van der Schaar
NeurIPS 2024poster

The discovery of dynamical systems is crucial across a range of fields, including pharmacology, epidemiology, and physical sciences. *Accurate* and *interpretable* modeling of these systems is essential for understanding complex temporal processes, optimizing interventions, and minimizing adverse ef…

Cited by 1Source ↗PDF ↗Scholar ↗
2024

ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference

Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar
ICLR 2024spotlight

Inferring unbiased treatment effects has received widespread attention in the machine learning community. In recent years, our community has proposed numerous solutions in standard settings, high-dimensional treatment settings, and even longitudinal settings. While very diverse, the solution has mos…

Cited by 7Source ↗PDF ↗Scholar ↗
2023

D-CIPHER: Discovery of Closed-form Partial Differential Equations

Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar
NeurIPS 2023poster

Closed-form differential equations, including partial differential equations and higher-order ordinary differential equations, are one of the most important tools used by scientists to model and better understand natural phenomena. Discovering these equations directly from data is challenging becaus…

Cited by 7Source ↗PDF ↗Scholar ↗
2023

GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure

Tennison Liu, Zhaozhi Qian, Jeroen Berrevoets, Mihaela van der Schaar
ICLR 2023poster

Deep generative models learn highly complex and non-linear representations to generate realistic synthetic data. While they have achieved notable success in computer vision and natural language processing, similar advances have been less demonstrable in the tabular domain. This is partially because…

Cited by 76Source ↗PDF ↗code ↗Scholar ↗
2023

Learning Representations without Compositional Assumptions

Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar
ICML 2023poster

This paper addresses unsupervised representation learning on tabular data containing multiple views generated by distinct sources of measurement. Traditional methods, which tackle this problem using the multi-view framework, are constrained by predefined assumptions that assume feature sets share th…

Cited by 2Source ↗PDF ↗Scholar ↗
2023

Membership Inference Attacks against Synthetic Data through Overfitting Detection

Boris van Breugel, Hao Sun, Zhaozhi Qian, Mihaela van der Schaar
AISTATS 2023poster

Data is the foundation of most science. Unfortunately, sharing data can be obstructed by the risk of violating data privacy, impeding research in fields like healthcare. Synthetic data is a potential solution. It aims to generate data that has the same distribution as the original data, but that doe…

Cited by 56Source ↗PDF ↗code ↗Scholar ↗
2023

Neural Laplace Control for Continuous-time Delayed Systems

Samuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, Mihaela van der Schaar
AISTATS 2023poster

Many real-world offline reinforcement learning (RL) problems involve continuous-time environments with delays. Such environments are characterized by two distinctive features: firstly, the state x(t) is observed at irregular time intervals, and secondly, the current action a(t) only affects the futu…

Cited by 15Source ↗PDF ↗code ↗Scholar ↗
2023

Synthcity: a benchmark framework for diverse use cases of tabular synthetic data

Zhaozhi Qian, Rob Davis, Mihaela van der Schaar
NeurIPS 2023poster

Accessible high-quality data is the bread and butter of machine learning research, and the demand for data has exploded as larger and more advanced ML models are built across different domains. Yet, real data often contain sensitive information, are subject to various biases, and are costly to acqui…

Cited by 27Source ↗PDF ↗code ↗Scholar ↗
2023

Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data

Boris van Breugel, Zhaozhi Qian, Mihaela van der Schaar
ICML 2023poster

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. Unfortunately, synthetic data is usually not perfect, resulting in potential errors in downstream tasks. In this work we expl…

Cited by 45Source ↗PDF ↗code ↗Scholar ↗
2023

TRIAGE: Characterizing and auditing training data for improved regression

Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar
NeurIPS 2023poster

Data quality is crucial for robust machine learning algorithms, with the recent interest in data-centric AI emphasizing the importance of training data characterization. However, current data characterization methods are largely focused on classification settings, with regression settings largely un…

Cited by 8Source ↗PDF ↗Scholar ↗
2022

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

Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar
ICML 2022spotlight

Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare by assisting decision-makers to answer "what-if" questions. Existing causal inference approaches typically consider regular, discrete-time intervals between observations and treatment decisions and hence…

Cited by 60Source ↗PDF ↗code ↗Scholar ↗
2022

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

Zhaozhi Qian, Krzysztof Kacprzyk, Mihaela van der Schaar
ICLR 2022spotlight

For centuries, scientists have manually designed closed-form ordinary differential equations (ODEs) to model dynamical systems. An automated tool to distill closed-form ODEs from observed trajectories would accelerate the modeling process. Traditionally, symbolic regression is used to uncover a clos…

Cited by 27Source ↗PDF ↗Scholar ↗
2022

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

Samuel I Holt, Zhaozhi Qian, Mihaela van der Schaar
ICML 2022oral

Neural Ordinary Differential Equations model dynamical systems with ODEs learned by neural networks. However, ODEs are fundamentally inadequate to model systems with long-range dependencies or discontinuities, which are common in engineering and biological systems. Broader classes of differential eq…

Cited by 37Source ↗PDF ↗code ↗Scholar ↗
2021

Clairvoyance: A Pipeline Toolkit for Medical Time Series

Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar
ICLR 2021poster

Time-series learning is the bread and butter of data-driven *clinical decision support*, and the recent explosion in ML research has demonstrated great potential in various healthcare settings. At the same time, medical time-series problems in the wild are challenging due to their highly *composite*…

Cited by 54Source ↗PDF ↗code ↗Scholar ↗
2021

Estimating Multi-cause Treatment Effects via Single-cause Perturbation

Zhaozhi Qian, Alicia Curth, Mihaela van der Schaar
NeurIPS 2021poster

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. However, many applications involve simultaneous intervention on multiple variables, which leads to multi-cause treatme…

Cited by 19Source ↗PDF ↗code ↗Scholar ↗
2021

Explaining Latent Representations with a Corpus of Examples

Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar
NeurIPS 2021spotlight

Modern machine learning models are complicated. Most of them rely on convoluted latent representations of their input to issue a prediction. To achieve greater transparency than a black-box that connects inputs to predictions, it is necessary to gain a deeper understanding of these latent representa…

Cited by 49Source ↗PDF ↗code ↗Scholar ↗
2021

Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression

Zhaozhi Qian, William R. Zame, Lucas M Fleuren, Paul Elbers, Mihaela van der Schaar
NeurIPS 2021poster

Modeling a system's temporal behaviour in reaction to external stimuli is a fundamental problem in many areas. Pure Machine Learning (ML) approaches often fail in the small sample regime and cannot provide actionable insights beyond predictions. A promising modification has been to incorporate exper…

Cited by 68Source ↗PDF ↗code ↗Scholar ↗
2021

Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis

Jeroen Berrevoets, Ahmed Alaa, Zhaozhi Qian, James Jordon, Alexander E. S. Gimson, Mihaela van der Schaar
ICML 2021spotlight

Organ transplantation is often the last resort for treating end-stage illnesses, but managing transplant wait-lists is challenging because of organ scarcity and the complexity of assessing donor-recipient compatibility. In this paper, we develop a data-driven model for (real-time) organ allocation u…

Cited by 21Source ↗PDF ↗code ↗Scholar ↗
2021

SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes

Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela Mary Wood, Mihaela van der Schaar
NeurIPS 2021poster

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. However, previous methods focus only on adjusting for the covariates while neglecting the temporal struct…

Cited by 30Source ↗PDF ↗code ↗Scholar ↗
2020

Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes

Zhaozhi Qian, Ahmed Alaa, Alexis Bellot, Mihaela Schaar, Jem Rashbass
AISTATS 2020poster

Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic medical records, we only observe onsets of diseases, but not their triggering comorbidities — i.e., the mechanisms underlying temporal relations between diseases need to be inferred. Lear…

Cited by 27Source ↗PDF ↗code ↗Scholar ↗
2020

Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift

Alex Chan, Ahmed Alaa, Zhaozhi Qian, Mihaela Van Der Schaar
ICML 2020poster

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 ability to quantify confidence in their predictions — this is crucial in high-stakes applications that involve critical de…

Cited by 57Source ↗PDF ↗Scholar ↗
2020

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
NeurIPS 2020oral

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 governments have acted promptly enough, and whether lockdown measures can be lifted soon have since been central in public…

Cited by 46Source ↗PDF ↗code ↗Scholar ↗