Determinantal point processes for machine learning
This provides a tractable probabilistic model for tasks requiring diversity and repulsion, addressing a bottleneck in structured models like Markov random fields.
The paper tackles the problem of modeling repulsion and negative correlations in machine learning by introducing determinantal point processes (DPPs), which offer efficient and exact algorithms for inference tasks like sampling and marginalization. It demonstrates their application in real-world scenarios such as generating diverse search results and informative document summaries.
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.