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pro vyhledávání: '"Nir Ailon"'
Autor:
Nir Ailon
Publikováno v:
Theoretical Computer Science. 814:234-248
The Fourier Transform is one of the most important linear transformations used in science and engineering. Cooley and Tukey's Fast Fourier Transform (FFT) from 1964 is a method for computing this transformation in time $O(n\log n)$. From a lower boun
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70560f13e09c6ca7c19b52db5ddb7449
https://doi.org/10.1016/j.tcs.2020.02.002
https://doi.org/10.1016/j.tcs.2020.02.002
Publikováno v:
Theoretical Computer Science. 634:55-66
The k-means++ seeding algorithm is one of the most popular algorithms that is used for finding the initial k centers when using the Lloyd's algorithm for the k-means problem. It was conjectured by Brunsch and Roglin 9 that k-means++ behaves well for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ba6d24b1c489f5657c0dba85cd2816c
https://doi.org/10.1016/j.tcs.2016.04.012
https://doi.org/10.1016/j.tcs.2016.04.012
Autor:
Nir Ailon
Publikováno v:
ACM Transactions on Computation Theory. 8:1-14
Obtaining a nontrivial (superlinear) lower bound for computation of the Fourier transform in the linear circuit model has been a long-standing open problem for more than 40 years. An early result by Morgenstern from 1973, provides an Ω( n log n ) lo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8fa7d89dd909971bac18e7805ba12e8b
https://doi.org/10.1145/2858785
https://doi.org/10.1145/2858785
Autor:
Nir Ailon, Gal Yehuda
Publikováno v:
Information Processing Letters. 165:106024
The complexity of computing the Fourier transform is a longstanding open problem. Very recently, Ailon (2013, 2014, 2015) showed in a collection of papers that, roughly speaking, a speedup of the Fourier transform computation implies numerical ill-co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6ac3bd4754289a7d353c772b0dce8b66
https://doi.org/10.1016/j.ipl.2020.106024
https://doi.org/10.1016/j.ipl.2020.106024
Publikováno v:
LATIN 2018: Theoretical Informatics ISBN: 9783319774039
LATIN
LATIN
Ashtiani et al. (NIPS 2016) introduced a semi-supervised framework for clustering (SSAC) where a learner is allowed to make same-cluster queries. More specifically, in their model, there is a query oracle that answers queries of the form “given any
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c5e4535524febd2914599605af8c6b7d
https://doi.org/10.1007/978-3-319-77404-6_2
https://doi.org/10.1007/978-3-319-77404-6_2
Publikováno v:
SIAM Journal on Computing. 41:1110-1121
In this work we study the problem of bipartite correlation clustering (BCC), a natural bipartite counterpart of the well-studied correlation clustering (CC) problem [N. Bansal, A. Blum, and S. Chawla, Machine Learning, 56 (2004), pp. 89--113], also r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e33e2928c4515faa47557165445aa1f1
https://doi.org/10.1137/110848712
https://doi.org/10.1137/110848712
Autor:
Moses Charikar, Nir Ailon
Publikováno v:
FOCS
Given dissimilarity data on pairs of objects in a set, we study the problem of fitting a tree metric to this data so as to minimize additive error (i.e. some measure of the difference between the tree metric and the given data). This problem arises i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25b94830f26d54a249de2b679de3d506
https://doi.org/10.1137/100806886
https://doi.org/10.1137/100806886
Publikováno v:
Discrete & Computational Geometry. 45:34-44
Random projection methods give distributions over k×d matrices such that if a matrix Ψ (chosen according to the distribution) is applied to a finite set of vectors x i ∈ℝd the resulting vectors Ψx i ∈ℝk approximately preserve the original
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f89739d7aba0572a75e1f38dad7bf0b
https://doi.org/10.1007/s00454-010-9309-5
https://doi.org/10.1007/s00454-010-9309-5
Autor:
Nir Ailon, Mehryar Mohri
Publikováno v:
Machine Learning. 80:189-211
This paper presents an efficient preference-based ranking algorithm running in two stages. In the first stage, the algorithm learns a preference function defined over pairs, as in a standard binary classification problem. In the second stage, it make
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d7bf01728004e444586dc734ea24fb5
https://doi.org/10.1007/s10994-010-5176-9
https://doi.org/10.1007/s10994-010-5176-9
Autor:
Nir Ailon, Bernard Chazelle
Publikováno v:
Communications of the ACM. 53:97-104
Data represented geometrically in high-dimensional vector spaces can be found in many applications. Images and videos, are often represented by assigning a dimension for every pixel (and time). Text documents may be represented in a vector space wher
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::814720193c85f4fd9ffcb7389890e8c3
https://doi.org/10.1145/1646353.1646379
https://doi.org/10.1145/1646353.1646379