1.5k citations · 1.7k across the 10 of their papers we have counts for
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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…
How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing
Yoram Bachrach, Thore Graepel, Tom Minka +1
We propose a new probabilistic graphical model that jointly models the difficulties of questions, the abilities of participants and the correct answers to questions in aptitude tes…
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…