Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Chaturvedi, Anamay"'
We consider the problem of clustering in the learning-augmented setting, where we are given a data set in $d$-dimensional Euclidean space, and a label for each data point given by an oracle indicating what subsets of points should be clustered togeth
Externí odkaz:
http://arxiv.org/abs/2210.17028
In this work, we study the problem of privately maximizing a submodular function in the streaming setting. Extensive work has been done on privately maximizing submodular functions in the general case when the function depends upon the private data o
Externí odkaz:
http://arxiv.org/abs/2210.14315
A {\em universal 1-bit compressive sensing (CS)} scheme consists of a measurement matrix $A$ such that all signals $x$ belonging to a particular class can be approximately recovered from $\textrm{sign}(Ax)$. 1-bit CS models extreme quantization effec
Externí odkaz:
http://arxiv.org/abs/2202.10611
Estimating the quantiles of a large dataset is a fundamental problem in both the streaming algorithms literature and the differential privacy literature. However, all existing private mechanisms for distribution-independent quantile computation requi
Externí odkaz:
http://arxiv.org/abs/2201.03380
Given a data set of size $n$ in $d'$-dimensional Euclidean space, the $k$-means problem asks for a set of $k$ points (called centers) so that the sum of the $\ell_2^2$-distances between points of a given data set of size $n$ and the set of $k$ center
Externí odkaz:
http://arxiv.org/abs/2105.15007
We introduce a new $(\epsilon_p, \delta_p)$-differentially private algorithm for the $k$-means clustering problem. Given a dataset in Euclidean space, the $k$-means clustering problem requires one to find $k$ points in that space such that the sum of
Externí odkaz:
http://arxiv.org/abs/2009.01220
We study the problem of differentially private constrained maximization of decomposable submodular functions. A submodular function is decomposable if it takes the form of a sum of submodular functions. The special case of maximizing a monotone, deco
Externí odkaz:
http://arxiv.org/abs/2005.14717
Autor:
Chaturvedi, Anamay, Scarlett, Jonathan
Graphical model selection in Markov random fields is a fundamental problem in statistics and machine learning. Two particularly prominent models, the Ising model and Gaussian model, have largely developed in parallel using different (though often rel
Externí odkaz:
http://arxiv.org/abs/2002.08663
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:6984-6992
We study the problem of differentially private constrained maximization of decomposable submodular functions. A submodular function is decomposable if it takes the form of a sum of submodular functions. The special case of maximizing a monotone, deco
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::408cf7b53a1970bc2467ea01bbda1ccc
https://doi.org/10.1609/aaai.v35i8.16860
https://doi.org/10.1609/aaai.v35i8.16860
