Juliusvk
Postdoc at ETH Zürich. Research on causal inference and machine learning. PhD from University of Cambridge & Max Planck Institute for Intelligent Systems.
ETH ZürichZürich, Switzerland
Pinned Repositories
recourse
Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
neuralstat
Reproduction study of neural statistician
Covid19-age-related-causal-effects
Code and data for "Simpson's paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects"
Semi-Generative-Modelling
Code for "Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features" (AISTATS 2019)
semisup-learn
Semi-supervised learning frameworks for python, which allow fitting scikit-learn classifiers to partially labeled data
SSLCauseEffect
Code for "Semi-supervised learning, causality and the conditional cluster assumption" (UAI 2020)
Juliusvk's Repositories
Juliusvk/Covid19-age-related-causal-effects
Code and data for "Simpson's paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects"
Juliusvk/SSLCauseEffect
Code for "Semi-supervised learning, causality and the conditional cluster assumption" (UAI 2020)
Juliusvk/Semi-Generative-Modelling
Code for "Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features" (AISTATS 2019)
Juliusvk/semisup-learn
Semi-supervised learning frameworks for python, which allow fitting scikit-learn classifiers to partially labeled data