1 code implementation • 23 Aug 2022 • Youngseok Kim, Wei Wang, Peter Carbonetto, Matthew Stephens
We introduce a new empirical Bayes approach for large-scale multiple linear regression.
1 code implementation • 27 May 2021 • Peter Carbonetto, Abhishek Sarkar, ZiHao Wang, Matthew Stephens
We report on the potential for using algorithms for non-negative matrix factorization (NMF) to improve parameter estimation in topic models.
3 code implementations • 4 Jun 2018 • Youngseok Kim, Peter Carbonetto, Matthew Stephens, Mihai Anitescu
It is substantially faster than the interior point method, and just as accurate.
Computation Methodology
no code implementations • NeurIPS 2009 • Peter Carbonetto, Matthew King, Firas Hamze
We describe a new algorithmic framework for inference in probabilistic models, and apply it to inference for latent Dirichlet allocation.
no code implementations • NeurIPS 2008 • Peter Carbonetto, Mark Schmidt, Nando D. Freitas
The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning.