One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes
Abstract
In this paper, we initiate the study of one-pass algorithms for solving the maximum-a-posteriori (MAP) inference problem for Non-symmetric Determinantal Point Processes (NDPPs). In particular, we formulate streaming and online versions of the problem and provide one-pass algorithms for solving these problems. In our streaming setting, 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, and only need to output a valid solution at the end of the stream. Our online setting has an additional requirement of maintaining a valid solution at any point in time. We design new one-pass algorithms for these problems and show that they perform comparably to (or even better than) the offline greedy algorithm while using substantially lower memory.
Cite
Text
Reddy et al. "One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes." International Conference on Machine Learning, 2022.Markdown
[Reddy et al. "One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes." International Conference on Machine Learning, 2022.](https://mlanthology.org/icml/2022/reddy2022icml-onepass/)BibTeX
@inproceedings{reddy2022icml-onepass,
title = {{One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes}},
author = {Reddy, Aravind and Rossi, Ryan A. and Song, Zhao and Rao, Anup and Mai, Tung and Lipka, Nedim and Wu, Gang and Koh, Eunyee and Ahmed, Nesreen},
booktitle = {International Conference on Machine Learning},
year = {2022},
pages = {18463-18482},
volume = {162},
url = {https://mlanthology.org/icml/2022/reddy2022icml-onepass/}
}