3 papers
cs.LG2025
Node Embeddings via Neighbor Embeddings
Jan Niklas Böhm, Marius Keute, Alica Guzmán +3
Node embeddings are a paradigm in non-parametric graph representation learning, where graph nodes are embedded into a given vector space to enable downstream processing. State-of-t…
cs.LG2025
On the Importance of Embedding Norms in Self-Supervised Learning
Andrew Draganov, Sharvaree Vadgama, Sebastian Damrich +4
Self-supervised learning (SSL) allows training data representations without a supervised signal and has become an important paradigm in machine learning. Most SSL methods employ th…
cs.CG2025
A Tight VC-Dimension Analysis of Clustering Coresets with Applications
Vincent Cohen-Addad, Andrew Draganov, Matteo Russo +2
We consider coresets for -clustering problems, where the goal is to assign points to centers minimizing powers of distances. A popular example is the -median objective $\sum_…