3 papers
cs.LG2020
Near-Optimal Comparison Based Clustering
Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar
The goal of clustering is to group similar objects into meaningful partitions. This process is well understood when an explicit similarity measure between the objects is given. How…
stat.ML2018
Foundations of Comparison-Based Hierarchical Clustering
Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
We address the classical problem of hierarchical clustering, but in a framework where one does not have access to a representation of the objects or their pairwise similarities. In…
stat.ML2018
Boosting for Comparison-Based Learning
Michaël Perrot, Ulrike von Luxburg
We consider the problem of classification in a comparison-based setting: given a set of objects, we only have access to triplet comparisons of the form "object is closer to o…