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Found 5 papers, 3 papers with code
A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
We introduce a new empirical Bayes approach for large-scale multiple linear regression.
Non-negative matrix factorization algorithms greatly improve topic model fits
We report on the potential for using algorithms for non-negative matrix factorization (NMF) to improve parameter estimation in topic models.
A fast algorithm for maximum likelihood estimation of mixture proportions using sequential quadratic programming
It is substantially faster than the interior point method, and just as accurate.
Computation Methodology
A Stochastic approximation method for inference in probabilistic graphical models
We describe a new algorithmic framework for inference in probabilistic models, and apply it to inference for latent Dirichlet allocation.
An interior-point stochastic approximation method and an L1-regularized delta rule
The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning.
