We present ENCO, an efficient structure learning method that leverages observational and interventional data and scales to graphs with a thousand variables. Published at CausalUAI 2021.
We explore the application of normalizing flows on categorical data, and propose GraphCNF a permutation-invariant generative model on graphs. Published at ICLR 2021.
We investigate deep-learning based heuristics for first-order automated theorem provers.