In label-noise learning, the noise transition matrix reveals how an instance transitions from its clean label to its noisy label. Accurately estimating an instance's noise transition matrix is crucial for estimating its clean label.

Solving transport problems, i.e. finding a map transporting one given distribution

This work is also the first effort to benchmark the stability of causal discovery algorithms with respect to the values of their hyperparameters.

In this work, we investigate the geometry of the k-th homology embedding and focus on cases reminiscent of spectral clustering. Namely, we analyze the connected sum of manifolds as a perturbation of the direct sum of their homology embeddings.

SGMs have been primarily applied to data living on Euclidean spaces, i.e. manifolds with flat geometry.