42 citations · 60 across the 12 of their papers we have counts for
6 papers · 1 filter
Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields
Sunrit Chakraborty, Aritra Guha, Rayleigh Lei +1
Analysis of heterogeneous patterns in complex spatio-temporal data finds usage across various domains in applied science and engineering, including training autonomous vehicles to…
Dirichlet Simplex Nest and Geometric Inference
Mikhail Yurochkin, Aritra Guha, Yuekai Sun +1
We propose Dirichlet Simplex Nest, a class of probabilistic models suitable for a variety of data types, and develop fast and provably accurate inference algorithms by accounting f…
Scalable inference of topic evolution via models for latent geometric structures
Mikhail Yurochkin, Zhiwei Fan, Aritra Guha +2
We develop new models and algorithms for learning the temporal dynamics of the topic polytopes and related geometric objects that arise in topic model based inference. Our model is…
Conic Scan-and-Cover algorithms for nonparametric topic modeling
Mikhail Yurochkin, Aritra Guha, XuanLong Nguyen
We propose new algorithms for topic modeling when the number of topics is unknown. Our approach relies on an analysis of the concentration of mass and angular geometry of the topic…
Multi-way Interacting Regression via Factorization Machines
Mikhail Yurochkin, XuanLong Nguyen, Nikolaos Vasiloglou
We propose a Bayesian regression method that accounts for multi-way interactions of arbitrary orders among the predictor variables. Our model makes use of a factorization mechanism…
Multilevel Clustering via Wasserstein Means
Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin +3
We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a pote…