Zobrazeno 1 - 10
of 14 273
pro vyhledávání: '"A. PITTAS"'
Autor:
Jiang, Aiwu1 (AUTHOR) aiwuu@163.com, Yang, Gang2 (AUTHOR), Pagani-Núñez, Emilio1 (AUTHOR), Jiang, Demeng1 (AUTHOR)
Publikováno v:
Journal of Natural History. 2017, Vol. 51 Issue 31/32, p1929-1941. 13p.
Autor:
Pittas, Thanasis, Pensia, Ankit
Algorithmic robust statistics has traditionally focused on the contamination model where a small fraction of the samples are arbitrarily corrupted. We consider a recent contamination model that combines two kinds of corruptions: (i) small fraction of
Externí odkaz:
http://arxiv.org/abs/2410.17230
Publikováno v:
International Journal of Engineering Technologies and Management Research. 8:77-89
This paper provides the theoretical foundation the patents , of Nicholas Pittas related to storage and continuous production of energy via compressed air. Specifically, the energy balance between consumed energy, used for the compression of atmospher
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f75f21bd902b138c891ea5c91c9f9673
https://doi.org/10.29121/ijetmr.v8.i7.2021.992
https://doi.org/10.29121/ijetmr.v8.i7.2021.992
We study Gaussian sparse estimation tasks in Huber's contamination model with a focus on mean estimation, PCA, and linear regression. For each of these tasks, we give the first sample and computationally efficient robust estimators with optimal error
Externí odkaz:
http://arxiv.org/abs/2403.10416
We study the problem of estimating the mean of an identity covariance Gaussian in the truncated setting, in the regime when the truncation set comes from a low-complexity family $\mathcal{C}$ of sets. Specifically, for a fixed but unknown truncation
Externí odkaz:
http://arxiv.org/abs/2403.02300
We study the clustering problem for mixtures of bounded covariance distributions, under a fine-grained separation assumption. Specifically, given samples from a $k$-component mixture distribution $D = \sum_{i =1}^k w_i P_i$, where each $w_i \ge \alph
Externí odkaz:
http://arxiv.org/abs/2312.11769
We study the fundamental problems of Gaussian mean estimation and linear regression with Gaussian covariates in the presence of Huber contamination. Our main contribution is the design of the first sample near-optimal and almost linear-time algorithm
Externí odkaz:
http://arxiv.org/abs/2312.01547
Akademický článek
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Autor:
Parkin, Harry
Publikováno v:
The Concise Oxford Dictionary of Family Names in Britain, 1 ed., 2021.
Externí odkaz:
http://www.oxfordreference.com/view/10.1093/acref/9780198868255.001.0001/acref-9780198868255-e-31404
We study the complexity of learning mixtures of separated Gaussians with common unknown bounded covariance matrix. Specifically, we focus on learning Gaussian mixture models (GMMs) on $\mathbb{R}^d$ of the form $P= \sum_{i=1}^k w_i \mathcal{N}(\bolds
Externí odkaz:
http://arxiv.org/abs/2306.13057