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Finite mixture models do not reliably learn the number of components
v1v2v3 (latest)

Finite mixture models do not reliably learn the number of components

International Conference on Machine Learning (ICML), 2020
8 July 2020
Diana Cai
Trevor Campbell
Tamara Broderick
ArXiv (abs)PDFHTML

Papers citing "Finite mixture models do not reliably learn the number of components"

12 / 12 papers shown
Consistency of Graphical Model-based Clustering: Robust Clustering using
  Bayesian Spanning Forest
Consistency of Graphical Model-based Clustering: Robust Clustering using Bayesian Spanning Forest
Yu Zheng
Leo L. Duan
Arkaprava Roy
316
0
0
27 Sep 2024
Structurally Aware Robust Model Selection for Mixtures
Structurally Aware Robust Model Selection for Mixtures
Jiawei Li
Jonathan H. Huggins
291
0
0
01 Mar 2024
Modeling Random Networks with Heterogeneous Reciprocity
Modeling Random Networks with Heterogeneous ReciprocityJournal of machine learning research (JMLR), 2023
Daniel Cirkovic
Tiandong Wang
151
6
0
19 Aug 2023
Minimum $Φ$-distance estimators for finite mixing measures
Minimum ΦΦΦ-distance estimators for finite mixing measures
Yun-Chun Wei
Sayan Mukherjee
X. Nguyen
287
0
0
20 Apr 2023
Robustifying likelihoods by optimistically re-weighting data
Robustifying likelihoods by optimistically re-weighting dataJournal of the American Statistical Association (JASA), 2023
Miheer Dewaskar
Christopher Tosh
Jeremias Knoblauch
David B. Dunson
238
7
0
19 Mar 2023
Robust probabilistic inference via a constrained transport metric
Robust probabilistic inference via a constrained transport metric
Abhisek Chakraborty
A. Bhattacharya
D. Pati
330
4
0
17 Mar 2023
Nested Dirichlet models for unsupervised attack pattern detection in
  honeypot data
Nested Dirichlet models for unsupervised attack pattern detection in honeypot dataAnnals of Applied Statistics (AOAS), 2023
Francesco Sanna Passino
Anastasia Mantziou
Daniyar Ghani
Philip Thiede
Ross Bevington
N. Heard
AAML
271
2
0
06 Jan 2023
Bayesian mixture models (in)consistency for the number of clusters
Bayesian mixture models (in)consistency for the number of clustersScandinavian Journal of Statistics (Scand. J. Stat.), 2022
Louise Alamichel
D. Bystrova
Julyan Arbel
Guillaume Kon Kam King
613
8
0
25 Oct 2022
Bayesian nonparametric mixture inconsistency for the number of
  components: How worried should we be in practice?
Bayesian nonparametric mixture inconsistency for the number of components: How worried should we be in practice?
Yannis Chaumeny
Johan Van der Molen Moris
A. Davison
Paul D. W. Kirk
306
3
0
29 Jul 2022
Clustering consistency with Dirichlet process mixtures
Clustering consistency with Dirichlet process mixturesBiometrika (Biometrika), 2022
Filippo Ascolani
Antonio Lijoi
Giovanni Rebaudo
T. Rigon
273
45
0
25 May 2022
Consistency of mixture models with a prior on the number of components
Consistency of mixture models with a prior on the number of componentsDependence Modeling (DM), 2022
Jeffrey W. Miller
230
67
0
06 May 2022
Selective inference for k-means clustering
Selective inference for k-means clusteringJournal of machine learning research (JMLR), 2022
Yiqun T. Chen
Daniela Witten
279
63
0
29 Mar 2022
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