30 citations · 129 across the 21 of their papers we have counts for
23 papers
Hierarchical Clustering Can Jointly Satisfy Richness, Consistency, and Scale Invariance
Daichi Kuroda, Maximilien Dreveton, Matthias Grossglauser +1
Despite its ubiquity, clustering lacks a universally accepted definition of what is a cluster. Kleinberg's Impossibility Theorem formalizes this difficulty by showing that no flat…
Hierarchical Linkage Clustering Beyond Binary Trees and Ultrametrics
Maximilien Dreveton, Matthias Grossglauser, Daichi Kuroda +1
Hierarchical clustering seeks to uncover nested structures in data by constructing a tree of clusters, where deeper levels reveal finer-grained relationships. Traditional methods,…
Optimal Graph Clustering without Edge Density Signals
Maximilien Dreveton, Elaine Siyu Liu, Matthias Grossglauser +1
This paper establishes the theoretical limits of graph clustering under the Popularity-Adjusted Block Model (PABM), addressing limitations of existing models. In contrast to the St…
Why the Metric Backbone Preserves Community Structure
Maximilien Dreveton, Charbel Chucri, Matthias Grossglauser +1
The metric backbone of a weighted graph is the union of all-pairs shortest paths. It is obtained by removing all edges that are not the shortest path between and . I…
Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Models
Maximilien Dreveton, Alperen Gözeten, Matthias Grossglauser +1
Clustering is a pivotal challenge in unsupervised machine learning and is often investigated through the lens of mixture models. The optimal error rate for recovering cluster label…
It's All Relative: Interpretable Models for Scoring Bias in Documents
Aswin Suresh, Chi-Hsuan Wu, Matthias Grossglauser
We propose an interpretable model to score the bias present in web documents, based only on their textual content. Our model incorporates assumptions reminiscent of the Bradley-Ter…