Determinantal point processes for machine learning

Abstract

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured models like Markov random fields, which become intractable and hard to approximate in the presence of negative correlations, DPPs offer efficient and exact algorithms for sampling, marginalization, conditioning, and other inference tasks. We provide a gentle introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community, and show how DPPs can be applied to real-world applications like finding diverse sets of high-quality search results, building informative summaries by selecting diverse sentences from documents, modeling non-overlapping human poses in images or video, and automatically building timelines of important news stories.

120 pages

References in corpus (5)

• Loopy Belief Propagation for Approximate Inference: An Empirical Study
• Non-monotone submodular maximization under matroid and knapsack constraints
• Learning Mixtures of Submodular Shells with Application to Document Summarization
• Complexity and Completeness of Immanants
• Janossy Densities I. Determinantal Ensembles

Cited by in corpus (6)

• Fast Mixing for Discrete Point Processes
• Diversifying Sparsity Using Variational Determinantal Point Processes
• Inference for determinantal point processes without spectral knowledge
• Notes on using Determinantal Point Processes for Clustering with Applications to Text Clustering
• Discovering Valuable Items from Massive Data
• Block-Wise MAP Inference for Determinantal Point Processes with Application to Change-Point Detection