1.5k citations · 1.6k across the 5 of their papers we have counts for
5 papers
Expectation Propagation for approximate Bayesian inference
Thomas P. Minka
This paper presents a new deterministic approximation technique in Bayesian networks. This method, "Expectation Propagation", unifies two previous techniques: assumed-density filte…
Expectation-Propogation for the Generative Aspect Model
Thomas P. Minka, John Lafferty
The generative aspect model is an extension of the multinomial model for text that allows word probabilities to vary stochastically across documents. Previous results with aspect m…
Structured Region Graphs: Morphing EP into GBP
Max Welling, Thomas P. Minka, Yee Whye Teh
GBP and EP are two successful algorithms for approximate probabilistic inference, which are based on different approximation strategies. An open problem in both algorithms has been…
Virtual Vector Machine for Bayesian Online Classification
Thomas P. Minka, Rongjing Xiang, Yuan +1
In a typical online learning scenario, a learner is required to process a large data stream using a small memory buffer. Such a requirement is usually in conflict with a learner's…
Sparse-posterior Gaussian Processes for general likelihoods
Yuan, Qi, Ahmed H. Abdel-Gawad +1
Gaussian processes (GPs) provide a probabilistic nonparametric representation of functions in regression, classification, and other problems. Unfortunately, exact learning with GPs…