Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Silvio Lattanzi"'
Publikováno v:
Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) ISBN: 9781611977554
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3925459cc8ed870e46daf73a41d120e0
https://doi.org/10.1137/1.9781611977554.ch36
https://doi.org/10.1137/1.9781611977554.ch36
Autor:
Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab Mirrokni, Andres Munoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii
We study the private $k$-median and $k$-means clustering problem in $d$ dimensional Euclidean space. By leveraging tree embeddings, we give an efficient and easy to implement algorithm, that is empirically competitive with state of the art non privat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e97f43a11ff58120ca0dfbd69148e27
http://arxiv.org/abs/2206.08646
http://arxiv.org/abs/2206.08646
Publikováno v:
Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e9640f8d068c947c5dcdbd0ec6a8b8f3
https://doi.org/10.1145/3517804.3526227
https://doi.org/10.1145/3517804.3526227
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031221040
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::91b1b4112c5cff67e6e951e009ba83de
https://doi.org/10.1007/978-3-031-22105-7_14
https://doi.org/10.1007/978-3-031-22105-7_14
Publikováno v:
EC
The secretary problem is probably the purest model of decision making under uncertainty. In this paper we ask which advice can we give the algorithm to improve its success probability? We propose a general model that unifies a broad range of problems
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::02377a7500e7e0ca6293929dd4a8cf83
https://doi.org/10.1145/3465456.3467623
https://doi.org/10.1145/3465456.3467623
Publikováno v:
WWW
Understanding information dynamics and their resulting cascades is a central topic in social network analysis. In a recent seminal work, Cheng et al. analyzed multiples cascades on Facebook over several months, and noticed that many of them exhibit a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3a5f506fe336c79f1ef2f2f9c31d2c5
http://hdl.handle.net/11573/1550244
http://hdl.handle.net/11573/1550244
The modern era is witnessing a revolution in the ability to scale computations to massively large data sets. A key breakthrough in scalability was the introduction of fast and easy-to-use distributed programming models such as the Massively Parallel
Publikováno v:
ITA
FOCS
FOCS
We propose online algorithms for Column Subset Selection (CSS) and Principal Component Analysis (PCA), two methods that are widely employed for data analysis, summarization, and visualization. Given a data matrix A that is revealed one column at a ti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d4e47596f5ee54bc9a9640cb49860f1b
https://doi.org/10.1109/ita50056.2020.9244974
https://doi.org/10.1109/ita50056.2020.9244974
Publikováno v:
SODA
Online algorithms are a hallmark of worst case optimization under uncertainty. On the other hand, in practice, the input is often far from worst case, and has some predictable characteristics. A recent line of work has shown how to use machine learne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c6c3198cbdcbf2cebe186643ae9f976f
https://doi.org/10.1137/1.9781611975994.114
https://doi.org/10.1137/1.9781611975994.114