Zobrazeno 1 - 10
of 2 718
pro vyhledávání: '"minimax rates"'
Autor:
Peng, Jingfu, Yang, Yuhong
Recent research shows the susceptibility of machine learning models to adversarial attacks, wherein minor but maliciously chosen perturbations of the input can significantly degrade model performance. In this paper, we theoretically analyse the limit
Externí odkaz:
http://arxiv.org/abs/2410.09402
Autor:
Szabo, Botond, Zhu, Yichen
Gaussian Processes (GPs) are widely used to model dependency in spatial statistics and machine learning, yet the exact computation suffers an intractable time complexity of $O(n^3)$. Vecchia approximation allows scalable Bayesian inference of GPs in
Externí odkaz:
http://arxiv.org/abs/2410.10649
The minimax sample complexity of group distributionally robust optimization (GDRO) has been determined up to a $\log(K)$ factor, for $K$ the number of groups. In this work, we venture beyond the minimax perspective via a novel notion of sparsity that
Externí odkaz:
http://arxiv.org/abs/2410.00690
Autor:
Moen, Per August Jarval
We study the detection of a change in the spatial covariance matrix of $n$ independent sub-Gaussian random variables of dimension $p$. Our first contribution is to show that $\log\log(8n)$ is the exact minimax testing rate for a change in variance wh
Externí odkaz:
http://arxiv.org/abs/2405.07757
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We show both adaptive and non-adaptive minimax rates of convergence for a family of weighted Laplacian-Eigenmap based nonparametric regression methods, when the true regression function belongs to a Sobolev space and the sampling density is bounded f
Externí odkaz:
http://arxiv.org/abs/2311.00140
In online binary classification under \emph{apple tasting} feedback, the learner only observes the true label if it predicts ``1". First studied by \cite{helmbold2000apple}, we revisit this classical partial-feedback setting and study online learnabi
Externí odkaz:
http://arxiv.org/abs/2310.19064
Autor:
Zhao, Bingxin, Yang, Yuhong
This paper studies minimax rates of convergence for nonparametric location-scale models, which include mean, quantile and expectile regression settings. Under Hellinger differentiability on the error distribution and other mild conditions, we show th
Externí odkaz:
http://arxiv.org/abs/2307.01399
Autor:
Yan, Hao, Levin, Keith
Latent space models play an important role in the modeling and analysis of network data. Under these models, each node has an associated latent point in some (typically low-dimensional) geometric space, and network formation is driven by this unobser
Externí odkaz:
http://arxiv.org/abs/2307.01942
Akademický článek
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