van_der_Schaar \LAB
We are creating cutting-edge machine learning methods and applying them to drive a revolution in healthcare.
Pinned Repositories
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
van_der_Schaar \LAB's Repositories
vanderschaarlab/mlforhealthlabpub
Machine Learning and Artificial Intelligence for Medicine.
vanderschaarlab/clairvoyance
Clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series
vanderschaarlab/Interpretability
Resources for Machine Learning Explainability
vanderschaarlab/ml-as-prostate-cancer
Code repository for paper "Development and clinical utility of machine learning algorithms for dynamic longitudinal real-time estimation of progression risks in active surveillance of early prostate cancer"
vanderschaarlab/Graphical-modelling-continuous-time
Graphical modelling with time series data using an ODE 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/Simplex
This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help of a corpus of examples. For more details, please read our NeurIPS 2021 paper: 'Explaining Latent Representations with a Corpus of Examples'.
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/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
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/conformal-rnn
Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.
vanderschaarlab/D-CODE-ICLR-2022
D-CODE: Discovering Closed-form ODEs from Observed Trajectories
vanderschaarlab/Data-SUITE
Data-SUITE: Data-centric identification of in-distribution incongruous examples
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/ebm-for-cate
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects
vanderschaarlab/hide-and-seek-submissions
Repository of NeurIPS 2020 "Hide-and-Seek" competition submissions.
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/mcm
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
vanderschaarlab/organsync
Synthetic Control for high dimensional individual treatment effects
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-interpretability
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/invconban
vanderschaarlab/inverse-online
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies (ICLR 2022) by Alex J. Chan, Alicia Curth, and Mihaela van der Schaar
vanderschaarlab/lori
Inferring Lexicographically-Ordered Rewards from Preferences
vanderschaarlab/SEFS
Codebase for SEFS: Self-Supervision Enhanced Feature Selection with Correlated Gates
vanderschaarlab/Symbolic-Pursuit
Github for the NIPS 2020 paper "Learning outside the black-box: at the pursuit of interpretable models"
vanderschaarlab/TE-CDE
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
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.