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Found 5 papers, 2 papers with code
Model-based clustering using non-parametric Hidden Markov Models
The aim of this work is to study the Bayes risk of clustering when using HMMs and to propose associated clustering procedures.
Fundamental limits for learning hidden Markov model parameters
We study the frontier between learnable and unlearnable hidden Markov models (HMMs).
Risk of the Least Squares Minimum Norm Estimator under the Spike Covariance Model
We study risk of the minimum norm linear least squares estimator in when the number of parameters $d$ depends on $n$, and $\frac{d}{n} \rightarrow \infty$.
Exchangeable modelling of relational data: checking sparsity, train-test splitting, and sparse exchangeable Poisson matrix factorization
A variety of machine learning tasks---e. g., matrix factorization, topic modelling, and feature allocation---can be viewed as learning the parameters of a probability distribution over bipartite graphs.
Bootstrap estimators for the tail-index and for the count statistics of graphex processes
Graphex processes resolve some pathologies in traditional random graph models, notably, providing models that are both projective and allow sparsity.
Statistics Theory Statistics Theory Primary 62F10, secondary 60G55, 60G70
