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Found 7 papers, 0 papers with code
Inadmissibility of the corrected Akaike information criterion
For the multivariate linear regression model with unknown covariance, the corrected Akaike information criterion is the minimum variance unbiased estimator of the expected Kullback--Leibler discrepancy.
Information criteria for non-normalized models
However, model selection methods for general non-normalized models have not been proposed so far.
Imputation estimators for unnormalized models with missing data
We propose estimation methods for such unnormalized models with missing data.
Unified estimation framework for unnormalized models with statistical efficiency
The parameter estimation of unnormalized models is a challenging problem.
Analysis of Noise Contrastive Estimation from the Perspective of Asymptotic Variance
First, we propose a method for reducing asymptotic variance by estimating the parameters of the auxiliary distribution.
Estimation of Non-Normalized Mixture Models and Clustering Using Deep Representation
Then, based on the observation that conventional classification learning with neural networks is implicitly assuming an exponential family as a generative model, we introduce a method for clustering unlabeled data by estimating a finite mixture of distributions in an exponential family.
Empirical Bayes Matrix Completion
We develop an empirical Bayes (EB) algorithm for the matrix completion problems.
