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cambridge_dl4nlp

Cambridge Computer Lab 2018

clairvoyance

Clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series

clustering-signed-networks

colaborative-filtering

counterfactual-recurrent-network

Code for ICLR 2020 paper: "Estimating counterfactual treatment outcomes over time through adversarially balanced representations" by I. Bica, A. M. Alaa, J. Jordon, M. van der Schaar

covid-19-brazil

Code for Lancet Global Health 2020 paper: "Ethnic and regional variation in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study" by Pedro Baqui, Ioana Bica, Valerio Marra, Ari Ercole and Mihaela van der Schaar

diffvae

Code for Nature Scientific Reports 2020 paper: "Unsupervised generative and graph neural methods for modelling cell differentiation" by Ioana Bica, Helena Andrés-Terré, Ana Cvejic, Pietro Liò

histological-classification

Histological classification of tumours using somatic mutation data

htce-learners

Code for NeurIPS 2022 paper: "Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation" by I. Bica, M. van der Schaar

invariant-causal-imitation-learning

Code for NeurIPS 2021 paper: "Invariant Causal Imitation Learning for Generalizable Policies" by I. Bica, D. Jarrett, M. van der Schaar

multi-omics-data-integration

Multi-omics data integration using cross-modal neural networks

scigan

Code for NeurIPS 2020 paper: "Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks" by I. Bica, J. Jordon, M. van der Schaar

time-series-deconfounder

Code for ICML 2020 paper: "Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders" by I. Bica, A. M. Alaa, M. van der Schaar

vae-geneexpression

Unsupervised neural methods for modelling cell differentiation