@inproceedings{ed5ab85cfe4b4837a73b71285ce1a4f3,
title = "Entropic Trace Estimates for Log Determinants",
abstract = "The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal point processes, Gaussian processes, generalised Markov random fields, graph models and many others. In this work, we estimate log determinants under the framework of maximum entropy, given information in the form of moment constraints from stochastic trace estimation. The estimates demonstrate a significant improvement on state-of-the-art alternative methods, as shown on a wide variety of matrices from the SparseSuite Matrix Collection. By taking the example of a general Markov random field, we also demonstrate how this approach can significantly accelerate inference in large-scale learning methods involving the log determinant.",
author = "Jack Fitzsimons and Diego Granziol and Kurt Cutajar and Michael Osborne and Maurizio Filippone and Stephen Roberts",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 ; Conference date: 18-09-2017 Through 22-09-2017",
year = "2017",
doi = "10.1007/978-3-319-71249-9_20",
language = "English (US)",
isbn = "9783319712482",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "323--338",
editor = "Michelangelo Ceci and Saso Dzeroski and Celine Vens and Ljupco Todorovski and Jaakko Hollmen",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings",
address = "Germany",
}