Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Zhu Yuancheng"'
Publikováno v:
E3S Web of Conferences, Vol 145, p 02058 (2020)
With the development of intelligent theory of computer vision and the popularization of surveillance cameras, intelligent video analysis and surveillance technology has been widely used. The traditional video monitoring system will produce a large am
Externí odkaz:
https://doaj.org/article/846ec0e25cae40d687f88f498b21281e
Publikováno v:
Ann. Statist. 52(1): 392-411 (February 2024)
Optimal estimation and inference for both the minimizer and minimum of a convex regression function under the white noise and nonparametric regression models are studied in a nonasymptotic local minimax framework, where the performance of a procedure
Externí odkaz:
http://arxiv.org/abs/2305.00164
Autor:
Liu, Ruiyao, Yao, Guofeng, Wang, Qingyang, Yang, Nuo, Zhang, Jundong, Zhang, Chaolei, Zhu, Yuancheng, Li, Xiang, Yu, Zhenglei, Guo, Yunting, Xu, Zezhou, Li, Peng, Mao, Chunling
Publikováno v:
In Additive Manufacturing 25 August 2024 94
Akademický článek
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Autor:
Zhu, Yuancheng, Lafferty, John
This paper studies the problem of nonparametric estimation of a smooth function with data distributed across multiple machines. We assume an independent sample from a white noise model is collected at each machine, and an estimator of the underlying
Externí odkaz:
http://arxiv.org/abs/1803.01302
Autor:
Su, Weijie J., Zhu, Yuancheng
Stochastic gradient descent (SGD) is an immensely popular approach for online learning in settings where data arrives in a stream or data sizes are very large. However, despite an ever- increasing volume of work on SGD, much less is known about the s
Externí odkaz:
http://arxiv.org/abs/1802.04876
Publikováno v:
In Sensors and Actuators: B. Chemical 15 December 2022 373
Akademický článek
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We extend the traditional worst-case, minimax analysis of stochastic convex optimization by introducing a localized form of minimax complexity for individual functions. Our main result gives function-specific lower and upper bounds on the number of s
Externí odkaz:
http://arxiv.org/abs/1605.07596
We present a framework for incorporating prior information into nonparametric estimation of graphical models. To avoid distributional assumptions, we restrict the graph to be a forest and build on the work of forest density estimation (FDE). We refor
Externí odkaz:
http://arxiv.org/abs/1511.03796