Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Jayadev Acharya"'
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 10, Iss 2 (2020)
We develop differentially private methods for estimating various distributional properties. Given a sample from a discrete distribution p, some functional f, and accuracy and privacy parameters alpha and epsilon, the goal is to estimate f(p) up to ac
Externí odkaz:
https://doaj.org/article/8423f407cda248c9a041eb9b0996ce0e
Autor:
Chenhao Qian, Yuhan Liu, Cecil Barnett-Neefs, Sudeep Salgia, Omer Serbetci, Aaron Adalja, Jayadev Acharya, Qing Zhao, Renata Ivanek, Martin Wiedmann
Publikováno v:
Critical Reviews in Food Science and Nutrition. :1-17
In this age of data, digital tools are widely promoted as having tremendous potential for enhancing food safety. However, the potential of these digital tools depends on the availability and quality of data, and a number of obstacles need to be overc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64f7f2eec6ba6bd8832de7ab04941fa2
https://doi.org/10.1080/10408398.2022.2103086
https://doi.org/10.1080/10408398.2022.2103086
Publikováno v:
ISIT
We study the role of interactivity in distributed statistical inference under information constraints, e.g., communication constraints and local differential privacy. We focus on the tasks of goodness-of-fit testing and estimation of discrete distrib
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::631e90e7c2c6287709bdb6b3dc1457c5
https://doi.org/10.1109/tit.2021.3123905
https://doi.org/10.1109/tit.2021.3123905
Publikováno v:
IEEE Journal on Selected Areas in Information Theory. 1:454-468
The entropy of a quantum system is a measure of its randomness, and has applications in measuring quantum entanglement. We study the problem of estimating the von Neumann entropy, $S(\rho)$ , and Renyi entropy, $S_{\alpha }(\rho)$ of an unknown mixed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c639de8468d2deb96788015760636b62
https://doi.org/10.1109/jsait.2020.3015235
https://doi.org/10.1109/jsait.2020.3015235
We study goodness-of-fit and independence testing of discrete distributions in a setting where samples are distributed across multiple users. The users wish to preserve the privacy of their data while enabling a central server to perform the tests. U
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c845c61cfb2c069777176dbebdf82570
http://arxiv.org/abs/2101.07981
http://arxiv.org/abs/2101.07981
Autor:
Dongmei Li, Sourbh Bhadane, Victor Hernandez Martinez, Grace Deng, Jayadev Acharya, Aaron B. Wagner, Peter Wu, David S. Matteson, Elaine L. Hill, Ajay Anand, Sean Ryan, Ziteng Sun
Publikováno v:
Stat
Stat (International Statistical Institute)
Stat (International Statistical Institute)
Social distancing measures have been imposed across the US in order to stem the spread of COVID‐19. We quantify the reduction in doubling rate, by state, that is associated with this intervention. Using the earlier of K‐12 school closures and res
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b01abb1ad1bb4c678a72a7bdb320426c
Autor:
Grace Deng, Sean Ryan, Dongmei Li, Peter Wu, Victor Hernandez Martinez, Jayadev Acharya, Sourbh Bhadane, Ajay Anand, Elaine L. Hill, Ziteng Sun, Aaron B. Wagner, David S. Matteson
Social distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19. In this work we analyze the effect of current social distancing measures in the United States. We quantify th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08476234774ee4e5ed9a53f512bcbdfa
https://doi.org/10.1101/2020.04.27.20081836
https://doi.org/10.1101/2020.04.27.20081836
Publikováno v:
IEEE Transactions on Information Theory. 63:38-56
It was shown recently that estimating the Shannon entropy $H(p)$ of a discrete $k$ -symbol distribution $p$ requires $\Theta (k/\log k)$ samples, a number that grows near-linearly in the support size. In many applications, $H(p)$ can be replaced by t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80000b290e2219f788c790291d0476b3
https://doi.org/10.1109/tit.2016.2620435
https://doi.org/10.1109/tit.2016.2620435
A central server needs to perform statistical inference based on samples that are distributed over multiple users who can each send a message of limited length to the center. We study problems of distribution learning and identity testing in this dis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49d790b1fdf71512230a79722f3814f7
http://arxiv.org/abs/1905.08302
http://arxiv.org/abs/1905.08302
Multiple players are each given one independent sample, about which they can only provide limited information to a central referee. Each player is allowed to describe its observed sample to the referee using a channel from a family of channels $\math
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::629e732a586ca9856a2554e2ba9b3ca4
http://arxiv.org/abs/1812.11476
http://arxiv.org/abs/1812.11476