machine-learning

Virtual workshop: Leveraging Observational Data With Machine Learning

We're excited to announce the Leveraging Observational Data with Machine Learning Virtual Workshop - brought to you by Michael Blum (Owkin), Julie Josse (Inria), and Raphael Porcher (Université de Paris) — on June 22nd and 23rd 2021! Register now (for free) & check out our keynote speakers: Workshop website.

I've been awarded a Google PhD fellowship in Machine Learning.

I'm happy to officially share the great news that I'm a Google PhD fellow in Machine Learning this year. I'm very grateful for the support. Congrats to all the other awardees! And thanks of course to my great advisors and collaborators for their support: Julie Josse, Jean-Pierre Nadal, Jean-Philippe Vert, Stefan Wager, Tobias Gauss, and others.

Causal inference for observational clinical data

In machine learning, there has been great progress in obtaining powerful predictive models, but these models rely on correlations between variables and do not allow for an understanding of the underlying mechanisms or how to intervene on the system …

Treatment effect estimation with missing attributes

Postponed to 2021 due to COVID-19.

Treatment effect estimation with missing attributes

Interpretable ML: insights from a causal inference perspective

TBA.

Presentation of the article 'Invariant Causal Prediction for Nonlinear Models' by Heinze-Deml et al.

Presentation of the article ['Invariant Causal Prediction for Nonlinear Models'](https://arxiv.org/abs/1706.08576) by Heinze-Deml et al.

TrauMatrix

A decision support tool for critical care management.

Presentation of Ben Haeffele's paper on Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Application

Presentation of Ben Haeffele's paper on Structured Low-Rank Matrix Factorization: Global Optimality, Algorithms, and Application.