5.7k citations · 20k across the 76 of their papers we have counts for
80 papers
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
Han Guo, Philip Greengard, Hongyi Wang +3
The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decompo…
How Democracies Polarize: A Multilevel Perspective
Sihao Huang, Alexander F. Siegenfeld, Andrew Gelman
Democracies employ elections at various scales to select officials at the corresponding levels of administration. The geographical distribution of political opinion, the policy iss…
Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity
Martin Modrák, Angie H. Moon, Shinyoung Kim +5
Simulation-based calibration checking (SBC) is a practical method to validate computationally-derived posterior distributions or their approximations. In this paper, we introduce a…
Fast methods for posterior inference of two-group normal-normal models
Philip Greengard, Jeremy Hoskins, Charles C. Margossian +2
We describe a class of algorithms for evaluating posterior moments of certain Bayesian linear regression models with a normal likelihood and a normal prior on the regression coeffi…
Nested : Assessing the convergence of Markov chain Monte Carlo when running many short chains
Charles C. Margossian, Matthew D. Hoffman, Pavel Sountsov +3
Recent developments in parallel Markov chain Monte Carlo (MCMC) algorithms allow us to run thousands of chains almost as quickly as a single chain, using hardware accelerators such…
The piranha problem: Large effects swimming in a small pond
Christopher Tosh, Philip Greengard, Ben Goodrich +3
In some scientific fields, it is common to have certain variables of interest that are of particular importance and for which there are many studies indicating a relationship with…