5 accepted papers
Differentiable approaches to causal discovery have shown promise in learning DAG structures via continuous optimization, but their theoretical guarantees are largely restricted to models with homoscedastic noise or known noise distribution. In particular, existing methods based on mean squared error…
Uncovering causal relationships in datasets that include both categorical and continuous variables is a challenging problem. The overwhelming majority of existing methods restrict their application to dealing with a single type of variable. Our contribution is a structural causal model designed to h…
The introduction of the NOTEARS algorithm resulted in a wave of research on differentiable Directed Acyclic Graph (DAG) learning. Differentiable DAG learning transforms the combinatorial problem of identifying the DAG underlying a Structural Causal Model (SCM) into a constrained continuous optimizat…
Diffusion Probabilistic Models (DPM) have shown remarkable efficacy in the synthesis of high-quality images. However, their inference process characteristically requires numerous, potentially hundreds, of iterative steps, which could exaggerate the problem of exposure bias due to the training and in…
In Federated Learning (FL) and many other distributed training frameworks, collaborators can hold their private data locally and only share the network weights trained with the local data after multiple iterations. Gradient inversion is a family of privacy attacks that recovers data from its generat…