no code implementations • 11 Dec 2023 • Miriam Shiffman, Ryan Giordano, Tamara Broderick
We then overcome the inherent non-differentiability of gene set enrichment analysis to develop an additional approximation for the robustness of top gene sets.
1 code implementation • 11 Apr 2023 • Ryan Giordano, Martin Ingram, Tamara Broderick
We show on a variety of real-world problems that DADVI reliably finds good solutions with default settings (unlike ADVI) and, together with LR covariances, is typically faster and more accurate than standard ADVI.
1 code implementation • 20 Feb 2023 • Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan Giordano, Kaushik Srinivasan, Tamay Özgökmen, Junfei Xia, Tamara Broderick
Given sparse observations of buoy velocities, oceanographers are interested in reconstructing ocean currents away from the buoys and identifying divergences in a current vector field.
no code implementations • 8 Jul 2021 • Ryan Giordano, Runjing Liu, Michael I. Jordan, Tamara Broderick
Bayesian models based on the Dirichlet process and other stick-breaking priors have been proposed as core ingredients for clustering, topic modeling, and other unsupervised learning tasks.
1 code implementation • 28 Jul 2019 • Ryan Giordano, Michael. I. Jordan, Tamara Broderick
The first-order approximation is known as the "infinitesimal jackknife" in the statistics literature and has been the subject of recent interest in machine learning for approximate CV.
4 code implementations • 15 Oct 2018 • Runjing Liu, Ryan Giordano, Michael. I. Jordan, Tamara Broderick
Bayesian models based on the Dirichlet process and other stick-breaking priors have been proposed as core ingredients for clustering, topic modeling, and other unsupervised learning tasks.
Methodology
3 code implementations • 1 Jun 2018 • Ryan Giordano, Will Stephenson, Runjing Liu, Michael. I. Jordan, Tamara Broderick
This linear approximation is sometimes known as the "infinitesimal jackknife" in the statistics literature, where it is mostly used to as a theoretical tool to prove asymptotic results.
Methodology
1 code implementation • 31 Jan 2018 • Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin Thomas, Prabhat
We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia.
Distributed, Parallel, and Cluster Computing Instrumentation and Methods for Astrophysics 85A35, 68W10, 62P35 J.2; D.1.3; G.3; I.2; D.2
4 code implementations • 8 Sep 2017 • Ryan Giordano, Tamara Broderick, Michael. I. Jordan
The estimates for MFVB posterior covariances rely on a result from the classical Bayesian robustness literature relating derivatives of posterior expectations to posterior covariances and include the Laplace approximation as a special case.
Methodology
no code implementations • 10 Nov 2016 • Jeffrey Regier, Kiran Pamnany, Ryan Giordano, Rollin Thomas, David Schlegel, Jon McAuliffe, Prabhat
Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results.
no code implementations • 23 Jun 2016 • Ryan Giordano, Tamara Broderick, Rachael Meager, Jonathan Huggins, Michael Jordan
Bayesian hierarchical models are increasing popular in economics.
1 code implementation • NeurIPS 2015 • Ryan Giordano, Tamara Broderick, Michael Jordan
We call our method linear response variational Bayes (LRVB).
no code implementations • 26 Feb 2015 • Ryan Giordano, Tamara Broderick
We develop a fast, general methodology for exponential families that augments MFVB to deliver accurate uncertainty estimates for model variables -- both for individual variables and coherently across variables.
no code implementations • 24 Oct 2014 • Ryan Giordano, Tamara Broderick
We develop a fast, general methodology for exponential families that augments MFVB to deliver accurate uncertainty estimates for model variables -- both for individual variables and coherently across variables.