3k citations · 24.2k across the 299 of their papers we have counts for
10 papers · 2 filters
Tree-dependent Component Analysis
Francis R. Bach, Michael I. Jordan
We present a generalization of independent component analysis (ICA), where instead of looking for a linear transform that makes the data components independent, we look for a trans…
A Generalized Mean Field Algorithm for Variational Inference in Exponential Families
Eric P. Xing, Michael I. Jordan, Stuart Russell
The mean field methods, which entail approximating intractable probability distributions variationally with distributions from a tractable family, enjoy high efficiency, guaranteed…
Active Learning for Crowd-Sourced Databases
Barzan Mozafari, Purnamrita Sarkar, Michael J. Franklin +2
Crowd-sourcing has become a popular means of acquiring labeled data for a wide variety of tasks where humans are more accurate than computers, e.g., labeling images, matching objec…
Graph partition strategies for generalized mean field inference
Eric P. Xing, Michael I. Jordan, Stuart Russell
An autonomous variational inference algorithm for arbitrary graphical models requires the ability to optimize variational approximations over the space of model parameters as well…
Bayesian Multicategory Support Vector Machines
Zhihua Zhang, Michael I. Jordan
We show that the multi-class support vector machine (MSVM) proposed by Lee et. al. (2004), can be viewed as a MAP estimation procedure under an appropriate probabilistic interpreta…
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…
Nonparametric Link Prediction in Dynamic Networks
Purnamrita Sarkar, Deepayan Chakrabarti, Michael Jordan
We propose a non-parametric link prediction algorithm for a sequence of graph snapshots over time. The model predicts links based on the features of its endpoints, as well as those…
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…
Modeling Events with Cascades of Poisson Processes
Aleksandr Simma, Michael I. Jordan
We present a probabilistic model of events in continuous time in which each event triggers a Poisson process of successor events. The ensemble of observed events is thereby modeled…