It is regarded as a promising distributed and privacy-preserving method.

We show for the first time that this leads both to improved convergence bounds under standard assumptions.

We propose estimating Gaussian graphical models (GGMs) that are fair with respect to sensitive nodal attributes .

Two key features make correlation clustering quite suitable in real-world applications.

We perform experiments on high-dimensional Gaussians, Lotka-V olterra dynamics, and a Pickover attractor.

The Segmentation Anything Model (SAM) requires labor-intensive data labeling. We present Unsupervised SAM (UnSAM) for promptable and automatic whole-image segmentation that does not require human annotations.