Mélanie Rey

ICLR Conference 2023 Conference Paper

Learning to Induce Causal Structure

• Nan Rosemary Ke
• Silvia Chiappa
• Jane X. Wang
• Jörg Bornschein
• Anirudh Goyal
• Mélanie Rey
• Theophane Weber
• Matthew M. Botvinick

The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data. Most existing causal induction algorithms operate by generating candidate graphs and evaluating them using either score-based methods (including continuous optimization) or independence tests. In our work, we instead treat the inference process as a black box and design a neural network architecture that learns the mapping from both observational and interventional data to graph structures via supervised training on synthetic graphs. The learned model generalizes to new synthetic graphs, is robust to train-test distribution shifts, and achieves state-of-the-art performance on naturalistic graphs for low sample complexity.

ICML Conference 2014 Conference Paper

Sparse meta-Gaussian information bottleneck

• Mélanie Rey
• Volker Roth 0001
• Thomas J. Fuchs

We present a new sparse compression technique based on the information bottleneck (IB) principle, which takes into account side information. This is achieved by introducing a sparse variant of IB which preserves the information in only a few selected dimensions of the original data through compression. By assuming a Gaussian copula we can capture arbitrary non-Gaussian margins, continuous or discrete. We apply our model to select a sparse number of biomarkers relevant to the evolution of malignant melanoma and show that our sparse selection provides reliable predictors.

ICML Conference 2012 Conference Paper

Copula Mixture Model for Dependency-seeking Clustering

• Mélanie Rey
• Volker Roth 0001