DeepAI AI Chat
Log In Sign Up

DAG Learning on the Permutahedron

by   Valentina Zantedeschi, et al.
University of Amsterdam

We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data. Our approach optimizes over the polytope of permutation vectors, the so-called Permutahedron, to learn a topological ordering. Edges can be optimized jointly, or learned conditional on the ordering via a non-differentiable subroutine. Compared to existing continuous optimization approaches our formulation has a number of advantages including: 1. validity: optimizes over exact DAGs as opposed to other relaxations optimizing approximate DAGs; 2. modularity: accommodates any edge-optimization procedure, edge structural parameterization, and optimization loss; 3. end-to-end: either alternately iterates between node-ordering and edge-optimization, or optimizes them jointly. We demonstrate, on real-world data problems in protein-signaling and transcriptional network discovery, that our approach lies on the Pareto frontier of two key metrics, the SID and SHD.


An Improved Algorithm for Incremental Cycle Detection and Topological Ordering in Sparse Graphs

We consider the problem of incremental cycle detection and topological o...

Neural Topological Ordering for Computation Graphs

Recent works on machine learning for combinatorial optimization have sho...

Sequential Learning of the Topological Ordering for the Linear Non-Gaussian Acyclic Model with Parametric Noise

Causal discovery, the learning of causality in a data mining scenario, h...

Gradient-Based Neural DAG Learning

We propose a novel score-based approach to learning a directed acyclic g...

Diffusion Models for Causal Discovery via Topological Ordering

Discovering causal relations from observational data becomes possible wi...

Individualized Inference in Bayesian Quantile Directed Acyclic Graphical Models

We propose an approach termed "qDAGx" for Bayesian covariate-dependent q...