Results 11 to 20 of about 1,524,562 (340)
Robust Shadow Estimation [PDF]
PRX Quantum, 2021 Efficiently estimating properties of large and strongly coupled quantum systems is a central focus in many-body physics and quantum information theory. While quantum computers promise speedups for many of these tasks, near-term devices are prone to noise
Senrui Chen +3 more
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Robust Relative Error Estimation [PDF]
Entropy, 2018 Relative error estimation has been recently used in regression analysis. A crucial issue of the existing relative error estimation procedures is that they are sensitive to outliers.
Kei Hirose, Hiroki Masuda
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Multi-layer CNN-LSTM network with self-attention mechanism for robust estimation of nonlinear uncertain systems [PDF]
Frontiers in NeuroscienceIntroductionWith the help of robot technology, intelligent rehabilitation of patients with lower limb motor dysfunction caused by stroke can be realized.
Lin Liu +11 more
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Absolute M split estimation as an alternative for robust M-estimation [PDF]
Advances in Geodesy and Geoinformation, 2022 The problem of outlying observations is very well-known in the surveying data processing. Outliers might have several sources, different magnitudes, and shares within the whole observation set.
Robert Duchnowski, Patrycja Wyszkowska
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On the Adversarial Robustness of Robust Estimators [PDF]
IEEE Transactions on Information Theory, 2020 Motivated by recent data analytics applications, we study the adversarial robustness of robust estimators. Instead of assuming that only a fraction of the data points are outliers as considered in the classic robust estimation setup, in this paper, we consider an adversarial setup in which an attacker can observe the whole dataset and can modify all ...
Lifeng Lai, Erhan Bayraktar
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Robust Estimation via Robust Gradient Estimation [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2020 SummaryWe provide a new computationally efficient class of estimators for risk minimization. We show that these estimators are robust for general statistical models, under varied robustness settings, including in the classical Huber ε-contamination model, and in heavy-tailed settings.
Adarsh Prasad +3 more
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A COMPARISON OF M-ESTIMATION AND S-ESTIMATION ON THE FACTORS AFFECTING IR DHF IN EAST JAVA IN 2017
The Indonesian Journal of Public Health, 2021 Robust regression on M estimation and S estimation is the Ordinary Least Square (OLS) regression on the data outlier. East Java is one of the provinces in Indonesia with a high case fatalitiy rate (1.34%). The raising of Dengue Haemoragic Fever (DHF) in
Mardiana Mardiana +3 more
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Robust pooling through the data mode
Intelligent Systems with Applications, 2023 The task of learning from point cloud data is always challenging due to the often occurrence of noise and outliers in the data. Such data inaccuracies can significantly influence the performance of state-of-the-art deep learning networks and their ...
Ayman Mukhaimar +4 more
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COMPARISON OF ROBUST ESTIMATION ON MULTIPLE REGRESSION MODEL
Barekeng, 2023 This study aimed to compare the robustness of the OLS method with a robust regression model on data that had outliers. The methods used on the robust regression model were M-estimation, MM-estimation, and S-estimation.
Padrul Jana +2 more
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Robust error estimation based on factor-graph models for non-line-of-sight localization
EURASIP Journal on Advances in Signal Processing, 2022 This paper presents a method to estimate the covariances of the inputs in a factor-graph formulation for localization under non-line-of-sight conditions. A general solution based on covariance estimation and M-estimators in linear regression problems, is
O. Arda Vanli, Clark N. Taylor
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