57 citations · 159 across the 36 of their papers we have counts for
7 papers · 1 filter
Distance-to-Set Priors and Constrained Bayesian Inference
Rick Presman, Jason Xu
Constrained learning is prevalent in many statistical tasks. Recent work proposes distance-to-set penalties to derive estimators under general constraints that can be specified as…
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…
A unified analysis of convex and non-convex lp-ball projection problems
Joong-Ho Won, Kenneth Lange, Jason Xu
The task of projecting onto norm balls is ubiquitous in statistics and machine learning, yet the availability of actionable algorithms for doing so is largely limited to t…
Fitting a stochastic model of intensive care occupancy to noisy hospitalization time series during the COVID-19 pandemic
Achal Awasthi, Volodymyr M. Minin, Jenny Huang +2
Intensive care occupancy is an important indicator of health care stress that has been used to guide policy decisions during the COVID-19 pandemic. Toward reliable decision-making…
Quasi-Newton acceleration of EM and MM algorithms via Broydens method
Medha Agarwal, Jason Xu
The principle of majorization-minimization (MM) provides a general framework for eliciting effective algorithms to solve optimization problems. However, they often suffer from slow…