no code implementations • 27 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.
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.
no code implementations • 27 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.
no code implementations • 1 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.
no code implementations • 11 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.
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.
no code implementations • 1 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.
no code implementations • 3 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.
1 code implementation • 30 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.
2 code implementations • 26 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.
2 code implementations • 31 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.
2 code implementations • 16 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.
1 code implementation • 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.
1 code implementation • 24 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.
1 code implementation • 16 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.
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.
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.
2 code implementations • 24 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.
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.
2 code implementations • 30 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.
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.
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?
2 code implementations • 10 Jun 2022 • Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar
Neural Ordinary Differential Equations model dynamical systems with ODEs learned by neural networks.
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.
2 code implementations • 18 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.
1 code implementation • 8 Jan 2020 • Zhaozhi Qian, Ahmed M. Alaa, Alexis Bellot, Jem Rashbass, Mihaela van der Schaar
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals.
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.
2 code implementations • 16 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.