4 papers
Robust Linear Predictions: Analyses of Uniform Concentration, Fast Rates and Model Misspecification
Saptarshi Chakraborty, Debolina Paul, Swagatam Das
The problem of linear predictions has been extensively studied for the past century under pretty generalized frameworks. Recent advances in the robust statistics literature allow u…
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…
Convex Clustering Redefined: Robust Learning with the Median of Means Estimator
Sourav De, Koustav Chowdhury, Bibhabasu Mandal +4
Clustering approaches that utilize convex loss functions have recently attracted growing interest in the formation of compact data clusters. Although classical methods like k-means…
Dirichlet Process-based Robust Clustering using the Median-of-Means Estimator
Supratik Basu, Jyotishka Ray Choudhury, Debolina Paul +1
Clustering stands as one of the most prominent challenges in unsupervised machine learning. Among centroid-based methods, the classic -means algorithm, based on Lloyd's heuristi…