115 citations · 702 across the 30 of their papers we have counts for
7 papers · 1 filter
Variational Bayesian Inference with Stochastic Search
John Paisley, David Blei, Michael Jordan
Mean-field variational inference is a method for approximate Bayesian posterior inference. It approximates a full posterior distribution with a factorized set of distributions by m…
The Big Data Bootstrap
Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar +1
The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets, the computation of bootstrap-based quantit…
The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features
Kurt T. Miller, Thomas Griffiths, Michael I. Jordan
Nonparametric Bayesian models are often based on the assumption that the objects being modeled are exchangeable. While appropriate in some applications (e.g., bag-of-words models f…
Optimization of Structured Mean Field Objectives
Alexandre Bouchard-Cote, Michael I. Jordan
In intractable, undirected graphical models, an intuitive way of creating structured mean field approximations is to select an acyclic tractable subgraph. We show that the hardness…
EP-GIG Priors and Applications in Bayesian Sparse Learning
Zhihua Zhang, Shusen Wang, Dehua Liu +1
In this paper we propose a novel framework for the construction of sparsity-inducing priors. In particular, we define such priors as a mixture of exponential power distributions wi…
Coherence Functions with Applications in Large-Margin Classification Methods
Zhihua Zhang, Guang Dai, Michael I. Jordan
Support vector machines (SVMs) naturally embody sparseness due to their use of hinge loss functions. However, SVMs can not directly estimate conditional class probabilities. In thi…