Clustering
On the Power of SVD in the Stochastic Block Model
A popular heuristic method for improving clustering results is to apply dimensionality reduction before running clustering algorithms.
Hierarchical clustering with dot products recovers hidden tree structure
In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure.
Overleaf Example
This allows us to include clustering in end-to-end trainable pipelines, with efficient gradients.
Addressing Negative Transfer in Diffusion Models
To achieve this, we propose leveraging existing MTL methods, but the presence of a huge number of denoising tasks makes this computationally expensive to calculate the necessary per-task loss or gradient.
p-value Adjustment for Monotonous, Unbiased, and Fast Clustering Comparison
Adjusted Mutual Information, are type II biased.
Recovering Unbalanced Communities in the Stochastic Block Model with Application to Clustering with a Faulty Oracle
The stochastic block model (SBM) is a fundamental model for studying graph clustering or community detection in networks. It has received great attention in the last decade and the balanced case, i.e., assuming all clusters have large