Wasserstein Learning of Determinantal Point Processes

TL;DR

This paper introduces a Wasserstein distance-based learning method for DPPs that improves predictive performance by leveraging subset similarity information, surpassing traditional MLE approaches.

Contribution

It proposes a differentiable relaxation of DPP sampling enabling Wasserstein-based learning, which better captures the true data distribution.

Findings

Wasserstein learning outperforms MLE in predictive tasks.

The approach effectively incorporates subset similarity information.

Experimental results demonstrate significant performance improvements.

Abstract

Determinantal point processes (DPPs) have received significant attention as an elegant probabilistic model for discrete subset selection. Most prior work on DPP learning focuses on maximum likelihood estimation (MLE). While efficient and scalable, MLE approaches do not leverage any subset similarity information and may fail to recover the true generative distribution of discrete data. In this work, by deriving a differentiable relaxation of a DPP sampling algorithm, we present a novel approach for learning DPPs that minimizes the Wasserstein distance between the model and data composed of observed subsets. Through an evaluation on a real-world dataset, we show that our Wasserstein learning approach provides significantly improved predictive performance on a generative task compared to DPPs trained using MLE.