Learning determinantal point processes by corrective negative sampling
Author(s)Mariet, Zelda; Gartrell, Mike; Sra, Suvrit
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Determinantal Point Processes (DPPs) have attracted significant interest from the machine-learning community due to their ability to elegantly and tractably model the delicate balance between quality and diversity of sets. DPPs are commonly learned from data using maximum likelihood estimation (MLE). While fitting observed sets well, MLE for DPPs may also assign high likelihoods to unobserved sets that are far from the true generative distribution of the data. To address this issue, which reduces the quality of the learned model, we introduce a novel optimization problem, Contrastive Estimation (CE), which encodes information about “negative” samples into the basic learning model. CE is grounded in the successful use of negative information in machine-vision and language modeling. Depending on the chosen negative distribution (which may be static or evolve during optimization), CE assumes two different forms, which we analyze theoretically and experimentally. We evaluate our new model on real-world datasets; on a challenging dataset, CE learning delivers a considerable improvement in predictive performance over a DPP learned without using contrastive information.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
22nd International Conference on Artificial Intelligence and Statistics
Mariet, Zelda et al. "Learning determinantal point processes by corrective negative sampling." 22nd International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, 89, MLResearch Pressh, 2019, 2251-2260. © 2019 The Author(s).
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