dc.contributor.author | Indyk, Piotr | |
dc.date.accessioned | 2021-01-25T19:53:49Z | |
dc.date.available | 2021-01-25T19:53:49Z | |
dc.date.issued | 2019-12 | |
dc.identifier.issn | 1049-5258 | |
dc.identifier.issn | 1049-5258 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/129554 | |
dc.description.abstract | We consider the task of estimating the entropy of k-ary distributions from samples in the streaming model, where space is limited. Our main contribution is an algorithm that requires O ( klog(1"3/")2 ) samples and a constant O(1) memory words of space and outputs a ±" estimate of H(p). Without space limitations, the sample complexity has been established as S(k, ") = T ( "logkk + log"22 k 0, which is sub-linear in the domain size k, and the current algorithms that achieve optimal sample complexity also require nearly-linear space in k. Our algorithm partitions [0, 1] into intervals and estimates the entropy contribution of probability values in each interval. The intervals are designed to trade off the bias and variance of these estimates. | en_US |
dc.description.sponsorship | National Science Foundation (U.S.). Computing and Communication Foundation (Grant 657471) | en_US |
dc.language.iso | en | |
dc.publisher | Neural Information Processing Systems Foundation | en_US |
dc.relation.isversionof | https://papers.nips.cc/paper/2019/hash/8e987cf1b2f1f6ffa6a43066798b4b7f-Abstract.html | en_US |
dc.rights | Article 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.source | Neural Information Processing Systems (NIPS) | en_US |
dc.title | Estimating entropy of distributions in constant space | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Acharya, Jayadev et al. “Estimating entropy of distributions in constant space.” Advances in Neural Information Processing Systems (NeurIPS 2019), December 2019, Vancouver, Canada, Neural Information Processing Systems Foundation, December 2019. © 2019 The Author(s) | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.relation.journal | Advances in Neural Information Processing Systems | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dc.date.updated | 2020-12-18T16:28:27Z | |
dspace.orderedauthors | Acharya, J; Bhadane, S; Indyk, P; Sun, Z | en_US |
dspace.date.submission | 2020-12-18T16:28:30Z | |
mit.journal.volume | 32 | en_US |
mit.license | PUBLISHER_POLICY | |
mit.metadata.status | Complete | |