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Found 4 papers, 1 papers with code
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning
Learning causal relationships between variables is a well-studied problem in statistics, with many important applications in science.
Large-Scale Differentiable Causal Discovery of Factor Graphs
Combining this novel structural assumption with recent advances that bridge the gap between causal discovery and continuous optimization, we achieve causal discovery on thousands of variables.
Estimation of Monge Matrices
Monge matrices and their permuted versions known as pre-Monge matrices naturally appear in many domains across science and engineering.
Statistical Optimal Transport via Factored Couplings
We propose a new method to estimate Wasserstein distances and optimal transport plans between two probability distributions from samples in high dimension.
