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Identifying Robust Radiomics Features for Lung Cancer by Using In-Vivo and Phantom Lung Lesions
Tomography, 2021 We propose a novel framework for determining radiomics feature robustness by considering the effects of both biological and noise signals. This framework is preliminarily tested in a study predicting the epidermal growth factor receptor (EGFR) mutation ...
Lin Lu +7 more
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Robust Growth Determinants [PDF]
SSRN Electronic Journal, 2011 This paper investigates the robustness of determinants of economic growth in the presence of model uncertainty, parameter heterogeneity and outliers. The robust model averaging approach introduced in the paper uses a flexible and parsimonious mixture modeling that allows for fat-tailed errors compared to the normal benchmark case. Applying robust model
Doppelhofer, Gernot, Weeks, Melvyn
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Stochastic Robustness: Towards a Comprehensive Robustness Tool [PDF]
, 1990 Stochastic robustness is a simple technique to determine the robustness of linear, time-invariant systems by Monte Carlo methods. Stochastic stability robustness has been described previously.
Ray, Laurie Ryan
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Resampled Efficient Frontier Integration for MOEAs
Entropy, 2021 Mean-variance portfolio optimization is subject to estimation errors for asset returns and covariances. The search for robust solutions has been traditionally tackled using resampling strategies that offer alternatives to reference sets of returns or ...
David Quintana, David Moreno
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Robust Forecast Comparison [PDF]
SSRN Electronic Journal, 2015 Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. In order to address this issue, a novel criterion for forecast evaluation that utilizes the entire distribution of forecast errors ...
JIN, Sainan +2 more
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Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning
IEEE Transactions on Information Forensics and Security, 2023 Recently, it has been widely known that deep neural networks are highly vulnerable and easily broken by adversarial attacks. To mitigate the adversarial vulnerability, many defense algorithms have been proposed. Recently, to improve adversarial robustness, many works try to enhance feature representation by imposing more direct supervision on the ...
Hong Joo Lee, Yong Man Ro
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One-Layer vs. Two-Layer SOM in the Context of Outlier Identification: A Simulation Study
Applied Sciences, 2021 In this work, we applied a stochastic simulation methodology to quantify the power of the detection of outlying mixture components of a stochastic model, when applying a reduced-dimension clustering technique such as Self-Organizing Maps (SOMs).
Gabriel Antonio Valverde Castilla +2 more
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The robustness of democratic consensus
, 2014 In linear models of consensus dynamics, the state of the various agents converges to a value which is a convex combination of the agents' initial states. We call it democratic if in the large scale limit (number of agents going to infinity) the vector of
Delvenne, Jean-Charles, Fagnani, Fabio
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Individualized microbiotas dictate the impact of dietary fiber on colitis sensitivity
MicrobiomeBackground The observation that the intestinal microbiota is central in the development of IBD suggests that dietary fiber, the microbiota’s primary source of nourishment, could play a central role in these diseases.
Erica Bonazzi +6 more
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Robustness Verification of Support Vector Machines [PDF]
, 2019 We study the problem of formally verifying the robustness to adversarial examples of support vector machines (SVMs), a major machine learning model for classification and regression tasks.
A Miné +25 more
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