@inproceedings{platt2007fast,
author = {Platt, John and Kiciman, Emre and Maltz, Dave},
title = {Fast Variational Inference for Large-scale Internet Diagnosis},
booktitle = {The Conference on Neural Information Processing Systems (NIPS)},
year = {2007},
month = {December},
abstract = {Web servers on the Internet need to maintain high reliability, but the cause of intermittent failures of web transactions is non-obvious. We use approximate Bayesian inference to diagnose problems with web services. This diagnosis problem is far larger than any previously attempted: it requires inference of 104 possible faults from 105 observations. Further, such inference must be performed in less than a second. Inference can be done at this speed by combining a mean-field variational approximation and the use of stochastic gradient descent to optimize a variational cost function. We use this fast inference to diagnose a time series of anomalous HTTP requests taken from a real web service. The inference is fast enough to analyze network logs with billions of entries in a matter of hours.},
url = {http://approjects.co.za/?big=en-us/research/publication/fast-variational-inference-for-large-scale-internet-diagnosis-2/},
edition = {The Conference on Neural Information Processing Systems (NIPS)},
}