Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Po-Ling Loh"'
Publikováno v:
IEEE Access, Vol 8, Pp 2835-2846 (2020)
We develop algorithms to control the scope of an infection spread on a network by allocating a fixed immunization budget to edges of the graph. We assume that the infection propagates according to an independent cascade model and interventions operat
Externí odkaz:
https://doaj.org/article/90fd0f5aae6d45be9e047af8a5783e15
Autor:
Mohsen Mazrooyisebdani, Veena A. Nair, Po-Ling Loh, Alexander B. Remsik, Brittany M. Young, Brittany S. Moreno, Keith C. Dodd, Theresa J. Kang, Justin C. William, Vivek Prabhakaran
Publikováno v:
Frontiers in Neuroscience, Vol 12 (2018)
Despite the established effectiveness of the brain-computer interface (BCI) therapy during stroke rehabilitation (Song et al., 2014a, 2015; Young et al., 2014a,b,c, 2015; Remsik et al., 2016), little is understood about the connections between motor
Externí odkaz:
https://doaj.org/article/471838b556e64851acebd2801291e2e2
Autor:
Po-Ling Loh
Publikováno v:
Entropy, Vol 19, Iss 11, p 617 (2017)
In recent years, tools from information theory have played an increasingly prevalent role in statistical machine learning. In addition to developing efficient, computationally feasible algorithms for analyzing complex datasets, it is of theoretical i
Externí odkaz:
https://doaj.org/article/db5289c7d9334b75bfda8e6e1a0d74ec
Publikováno v:
J Digit Imaging
The purpose of this study is to investigate the robustness of a commonly used convolutional neural network for image segmentation with respect to nearly unnoticeable adversarial perturbations, and suggest new methods to make these networks more robus
Publikováno v:
Information and Inference: A Journal of the IMA. 11:959-1036
Estimating the mean of a probability distribution using i.i.d. samples is a classical problem in statistics, wherein finite-sample optimal estimators are sought under various distributional assumptions. In this paper, we consider the problem of mean
Publikováno v:
2022 IEEE International Symposium on Information Theory (ISIT).
Publikováno v:
IEEE Journal on Selected Areas in Information Theory. 1:131-144
We propose a novel strategy for extracting features in supervised learning that can be used to construct a classifier which is more robust to small perturbations in the input space. Our method builds upon the idea of the information bottleneck by int
Publikováno v:
Nuclear Engineering and Design. 398:111975
Autor:
Zheng Liu, Po-Ling Loh
Robust estimation is an important problem in statistics which aims at providing a reasonable estimator when the data-generating distribution lies within an appropriately defined ball around an uncontaminated distribution. Although minimax rates of es
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49b4adcb47922feb8821dfaf31d49518
We investigate problems in penalized $M$-estimation, inspired by applications in machine learning debugging. Data are collected from two pools, one containing data with possibly contaminated labels, and the other which is known to contain only cleanl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4f1e754dae1a6bfec20105ba54d74d2
http://arxiv.org/abs/2006.09009
http://arxiv.org/abs/2006.09009