Show simple item record

dc.contributor.authorMariet, Zelda
dc.contributor.authorGartrell, Mike
dc.contributor.authorSra, Suvrit
dc.date.accessioned2021-04-08T15:13:46Z
dc.date.available2021-04-08T15:13:46Z
dc.date.issued2019-04
dc.identifier.urihttps://hdl.handle.net/1721.1/130415
dc.description.abstractDeterminantal 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.en_US
dc.language.isoen
dc.publisherMLResearch Pressen_US
dc.relation.isversionofhttp://proceedings.mlr.press/v89/mariet19b.htmlen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceProceedings of Machine Learning Researchen_US
dc.titleLearning determinantal point processes by corrective negative samplingen_US
dc.typeArticleen_US
dc.identifier.citationMariet, 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).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.relation.journal22nd International Conference on Artificial Intelligence and Statisticsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-04-07T12:26:31Z
dspace.orderedauthorsMariet, Z; Gartrell, M; Sra, Sen_US
dspace.date.submission2021-04-07T12:26:32Z
mit.journal.volume89en_US
mit.licensePUBLISHER_POLICY


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record