Zobrazeno 1 - 10
of 975
pro vyhledávání: '"Pittas, A."'
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
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
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
We study principal component analysis (PCA), where given a dataset in $\mathbb{R}^d$ from a distribution, the task is to find a unit vector $v$ that approximately maximizes the variance of the distribution after being projected along $v$. Despite bei
Externí odkaz:
http://arxiv.org/abs/2305.02544
We study the problem of list-decodable Gaussian covariance estimation. Given a multiset $T$ of $n$ points in $\mathbb R^d$ such that an unknown $\alpha<1/2$ fraction of points in $T$ are i.i.d. samples from an unknown Gaussian $\mathcal{N}(\mu, \Sigm
Externí odkaz:
http://arxiv.org/abs/2305.00966
Autor:
Evdokia Pittas, Leah Tompkins
Publikováno v:
Frontiers in Education, Vol 9 (2024)
IntroductionThis paper aims to provide a first systematic research overview of student learning outcomes in programs teaching school subjects through languages other than English (LOTE) which are not the mother tongue of the students, according to sc
Externí odkaz:
https://doaj.org/article/02a39ea46f3f40b2aeb5df9322d70a7c
We study the problem of list-decodable sparse mean estimation. Specifically, for a parameter $\alpha \in (0, 1/2)$, we are given $m$ points in $\mathbb{R}^n$, $\lfloor \alpha m \rfloor$ of which are i.i.d. samples from a distribution $D$ with unknown
Externí odkaz:
http://arxiv.org/abs/2206.05245