57 citations · 159 across the 36 of their papers we have counts for
11 papers · 1 filter
Affinity Graph Connectivity in Convex Clustering
Sam Rosen, Jason Xu
We generalize finite-sample bounds for convex clustering to the setting where affinity weights appearing in the objective correspond to a general connected graph. These bounds and…
Bayesian Transfer Learning
Piotr M. Suder, Jason Xu, David B. Dunson
Transfer learning is a burgeoning concept in statistical machine learning that seeks to improve inference and/or predictive accuracy on a domain of interest by leveraging data from…
The Stochastic Proximal Distance Algorithm
Haoyu Jiang, Jason Xu
Stochastic versions of proximal methods have gained much attention in statistics and machine learning. These algorithms tend to admit simple, scalable forms, and enjoy numerical st…
Bregman Power k-Means for Clustering Exponential Family Data
Adithya Vellal, Saptarshi Chakraborty, Jason Xu
Recent progress in center-based clustering algorithms combats poor local minima by implicit annealing, using a family of generalized means. These methods are variations of Lloyd's…
Uniform Concentration Bounds toward a Unified Framework for Robust Clustering
Debolina Paul, Saptarshi Chakraborty, Swagatam Das +1
Recent advances in center-based clustering continue to improve upon the drawbacks of Lloyd's celebrated -means algorithm over years after its introduction. Various methods…
Kernel k-Means, By All Means: Algorithms and Strong Consistency
Debolina Paul, Saptarshi Chakraborty, Swagatam Das +1
Kernel -means clustering is a powerful tool for unsupervised learning of non-linearly separable data. Since the earliest attempts, researchers have noted that such algorithms of…