van_der_Schaar \LAB

autoprognosis

A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.

clairvoyance

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

Datagnosis

A Data-Centric library providing a unified interface for state-of-the-art methods for hardness characterisation of data points.

evaluating-generative-models

hyperimpute

A framework for prototyping and benchmarking imputation methods

Interpretability

Resources for Machine Learning Explainability

MIRACLE

mlforhealthlabpub

Machine Learning and Artificial Intelligence for Medicine.

synthcity

A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.

temporai

TemporAI: ML-centric Toolkit for Medical Time Series

vanderschaarlab/mlforhealthlabpub

Machine Learning and Artificial Intelligence for Medicine.

vanderschaarlab/hyperimpute

A framework for prototyping and benchmarking imputation methods

vanderschaarlab/clairvoyance

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

vanderschaarlab/evaluating-generative-models

vanderschaarlab/survivalgan

SurvivalGAN: Generating Time-to-Event Data for Survival Analysis

vanderschaarlab/DECAF

DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks

vanderschaarlab/Composite-Feature-Selection

Official Code for the paper: "Composite Feature Selection using Deep Ensembles"

vanderschaarlab/DOMIAS

DOMIAS, a density-based MIA model that aims to infer membership by targeting local overfitting of the generative model.

vanderschaarlab/INVASE

INVASE: Instance-wise Variable Selection . For more details, read the paper "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019.

vanderschaarlab/SSCP

SSCP: Improving Adaptive Conformal Prediction Using Self-supervised Learning

vanderschaarlab/synthcity-benchmarking

vanderschaarlab/CARs

This repository contains the implementation of Concept Activation Regions, a new framework to explain deep neural networks with human concepts. For more details, please read our NeurIPS 2022 paper: 'Concept Activation Regions: a Generalized Framework for Concept-Based Explanations.

vanderschaarlab/clairvoyance2

clairvoyance2: a Unified Toolkit for Medical Time Series

vanderschaarlab/data-centric-synthetic-data

Code for the paper: Reimagining Synthetic Data Generation through Data-Centric AI: A Comprehensive Benchmark (NeurIPS 2023)

vanderschaarlab/synthetic-model-combination

Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning (NeurIPS 2022) by Alex J. Chan and Mihaela van der Schaar.

vanderschaarlab/d-struct

vanderschaarlab/deep_generative_ensemble

vanderschaarlab/Dynamask

This repository contains the implementation of Dynamask, a method to identify the features that are salient for a model to issue its prediction when the data is represented in terms of time series. For more details on the theoretical side, please read our ICML 2021 paper: 'Explaining Time Series Predictions with Dynamic Masks'.

vanderschaarlab/Fourier-flows

Generative Time-series Modeling with Fourier Flows

vanderschaarlab/ITErpretability

This repository contains the implementation of ITErpretability, a new framework to benchmark treatment effect deep neural network estimators with interpretability. For more details, please read our NeurIPS 2022 paper: 'Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability'.

vanderschaarlab/organsync

Synthetic Control for high dimensional individual treatment effects

vanderschaarlab/TANGOS

Implementation of Tabular Neural Gradient Orthogonalization and Specialization (TANGOS). A regularizer for neural networks described in our ICLR 2023 paper.

vanderschaarlab/.github

The van der Schaar Lab: Machine learning and AI for medicine

vanderschaarlab/CompCATE

Code to replicate the results in the AISTATS23 paper "Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data" (Curth & van der Schaar, 2023)

vanderschaarlab/demo-simplex-pub

vanderschaarlab/HTCE-learners

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

vanderschaarlab/LEGATO

vanderschaarlab/RobustXAI

This repository contains the implementation of the explanation invariance and equivariance metrics, a framework to evaluate the robustness of interpretability methods.

vanderschaarlab/Symbolic-Pursuit

Github for the NIPS 2020 paper "Learning outside the black-box: at the pursuit of interpretable models"

vanderschaarlab/transductive-dropout

Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift (ICML 2020) by Alex J. Chan, Ahmed M. Alaa, Zhaozhi Qian, and Mihaela van der Schaar.