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
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.
survivalgan
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
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/synthcity
A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.
vanderschaarlab/autoprognosis
A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.
vanderschaarlab/Datagnosis
A Data-Centric library providing a unified interface for state-of-the-art methods for hardness characterisation of data points.
vanderschaarlab/climb
CliMB: An AI-enabled Partner for Clinical Predictive Modeling
vanderschaarlab/Data-IQ
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
vanderschaarlab/CATENets
Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
vanderschaarlab/L2MAC
🚀 The LLM Automatic Computer Framework: L2MAC
vanderschaarlab/NeuralLaplace
Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
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/DeepGenerativeSymbolicRegression
Deep Generative Symbolic Regression Code
vanderschaarlab/informed-meta-learning
vanderschaarlab/TRIAGE
TRIAGE: Characterizing and auditing training data for improved regression
vanderschaarlab/conformal-rnn
Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.
vanderschaarlab/Data-SUITE
Data-SUITE: Data-centric identification of in-distribution incongruous examples
vanderschaarlab/mcm
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
vanderschaarlab/medkit-learn
The Medkit-Learn(ing) Environment. An open-source library for offline sequential decision making with a focus on medicine.
vanderschaarlab/optcommit
When to make and break commitments?
vanderschaarlab/tphenotype
vanderschaarlab/ActiveObservingInContinuous-timeControl
Active Observing in Continuous-time Control
vanderschaarlab/cvar_sensing
vanderschaarlab/D-CIPHER
vanderschaarlab/DAGNOSIS
vanderschaarlab/DataDrivenDiscovery
vanderschaarlab/HDTwinGen
vanderschaarlab/NeuralLaplaceControl
Neural Laplace Control
vanderschaarlab/POCA
vanderschaarlab/Self_Healing_ML
vanderschaarlab/SMART_Testing
vanderschaarlab/Symbolic-Pursuit
Github for the NIPS 2020 paper "Learning outside the black-box: at the pursuit of interpretable models"
vanderschaarlab/visualizing-token-importance
Auditing language models with distribution-based sensitivity analysis