Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes
This work addresses efficient processing of streaming data for NDPPs, which is incremental as it adapts existing methods to online settings.
The paper tackles the problem of online and streaming MAP inference and learning for Non-symmetric Determinantal Point Processes (NDPPs) with single-pass and sub-linear memory constraints, achieving performance comparable to state-of-the-art offline algorithms on real-world datasets.
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.