Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes
Machine Learning
2021-11-30 v1 Artificial Intelligence
Data Structures and Algorithms
Machine Learning
Abstract
In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory. The online setting has an additional requirement of maintaining a valid solution at any point in time. For solving these new problems, we propose algorithms with theoretical guarantees, evaluate them on several real-world datasets, and show that they give comparable performance to state-of-the-art offline algorithms that store the entire data in memory and take multiple passes over it.
Cite
@article{arxiv.2111.14674,
title = {Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes},
author = {Aravind Reddy and Ryan A. Rossi and Zhao Song and Anup Rao and Tung Mai and Nedim Lipka and Gang Wu and Eunyee Koh and Nesreen Ahmed},
journal= {arXiv preprint arXiv:2111.14674},
year = {2021}
}