Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Tian, Qinglong"'
Quantum nonlocality and nonclassicality are two remarkable characteristics of quantum theory, and offer quantum advantages in some quantum information processing. Motivated by recent work on the interplay between nonclassicality quantified by average
Externí odkaz:
http://arxiv.org/abs/2410.11219
This study introduces a new approach to addressing positive and unlabeled (PU) data through the double exponential tilting model (DETM). Traditional methods often fall short because they only apply to selected completely at random (SCAR) PU data, whe
Externí odkaz:
http://arxiv.org/abs/2407.09735
Autor:
Tian, Qinglong, Zhao, Jiwei
Protecting individual privacy is crucial when releasing sensitive data for public use. While data de-identification helps, it is not enough. This paper addresses parameter estimation in scenarios where data are perturbed using the Post-Randomization
Externí odkaz:
http://arxiv.org/abs/2403.07288
The presence of distribution shifts poses a significant challenge for deploying modern machine learning models in real-world applications. This work focuses on the target shift problem in a regression setting (Zhang et al., 2013; Nguyen et al., 2016)
Externí odkaz:
http://arxiv.org/abs/2401.16410
We study the domain adaptation problem with label shift in this work. Under the label shift context, the marginal distribution of the label varies across the training and testing datasets, while the conditional distribution of features given the labe
Externí odkaz:
http://arxiv.org/abs/2305.19123
Especially when facing reliability data with limited information (e.g., a small number of failures), there are strong motivations for using Bayesian inference methods. These include the option to use information from physics-of-failure or previous ex
Externí odkaz:
http://arxiv.org/abs/2204.06099
Publikováno v:
In Applied Mathematical Modelling December 2024 136
Statistical prediction plays an important role in many decision processes such as university budgeting (depending on the number of students who will enroll), capital budgeting (depending on the remaining lifetime of a fleet of systems), the needed am
Externí odkaz:
http://arxiv.org/abs/2109.13970
This article introduces methods for constructing prediction bounds or intervals for the number of future failures from heterogeneous reliability field data. We focus on within-sample prediction where early data from a failure-time process is used to
Externí odkaz:
http://arxiv.org/abs/2011.03140
This paper reviews two main types of prediction interval methods under a parametric framework. First, we describe methods based on an (approximate) pivotal quantity. Examples include the plug-in, pivotal, and calibration methods. Then we describe met
Externí odkaz:
http://arxiv.org/abs/2011.03065