Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes

Aravind Reddy, Ryan Rossi, Zhao Song, Anup Rao, Tung Mai, Nedim Lipka, Gang Wu, Eunyee Koh, Nesreen Ahmed

Keywords: online algorithms, nonsymmetric determinantal point processes

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.

One-sentence Summary: We introduce online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs), design algorithms with both theoretical and empirical guarantees, and prove a space lower bound for our inference problem.