5 papers · 1 filter
A New Framework for Convex Clustering in Kernel Spaces: Finite Sample Bounds, Consistency and Performance Insights
Shubhayan Pan, Kushal Bose, Debolina Paul +2
Convex clustering is a well-regarded clustering method, resembling the similar centroid-based approach of Lloyd's -means, without requiring a predefined cluster count. It starts…
Learning from Heterophilic Graphs: A Spectral Theory Perspective on the Impact of Self-Loops and Parallel Edges
Kushal Bose, Swagatam Das
Graph heterophily poses a formidable challenge to the performance of Message-passing Graph Neural Networks (MP-GNNs). The familiar low-pass filters like Graph Convolutional Network…
Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks
Kushal Bose, Swagatam Das
Graph Neural Networks (GNNs) suffer from oversquashing, where structural bottlenecks limit message propagation between distant nodes, hindering tasks that require long-range intera…
Transformers Are Universally Consistent
Sagar Ghosh, Kushal Bose, Swagatam Das
Despite their central role in the success of foundational models and large-scale language modeling, the theoretical foundations governing the operation of Transformers remain only…
Topology-Driven Clustering: Enhancing Performance with Betti Number Filtration
Arghya Pratihar, Kushal Bose, Swagatam Das
Clustering aims at partitioning data points into groups of similar objects without knowing about the class labels. However, clustering datasets with complex geometric structures, s…