Results 201 to 210 of about 896,445 (279)

Jensen's inequality for g-expectation: part 1

open access: yes, 2003
Zengjing Chen, R. Kulperger, Long Jiang
semanticscholar   +1 more source

Personalized Differential Privacy for Ridge Regression Under Output Perturbation

open access: yesNaval Research Logistics (NRL), Volume 73, Issue 4, Page 525-537, June 2026.
ABSTRACT The increased application of machine learning (ML) in sensitive domains requires protecting the training data through privacy frameworks, such as differential privacy (DP). Traditional DP enforces a uniform privacy level ε$$ \varepsilon $$, which bounds the maximum privacy loss that each data point in the dataset is allowed to incur.
Krishna Acharya   +3 more
wiley   +1 more source

On MAP Estimates and Source Conditions for Drift Identification in SDEs

open access: yesProceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.
ABSTRACT We consider the inverse problem of identifying the drift in an stochastic differential equation (SDE) from n$n$ observations of its solution at M+1$M+1$ distinct time points. We derive a corresponding maximum a posteriori (MAP) estimate, we prove differentiability properties as well as a so‐called tangential cone condition for the forward ...
Daniel Tenbrinck   +3 more
wiley   +1 more source

An analysis of silk density in spider webs. [PDF]

open access: yesR Soc Open Sci
Lin F   +7 more
europepmc   +1 more source

Recursive Feasibility of Nonlinear Stochastic Model Predictive Control With Gaussian Process Dynamics

open access: yesInternational Journal of Robust and Nonlinear Control, Volume 36, Issue 9, Page 4957-4970, June 2026.
ABSTRACT Data‐based learning of system dynamics allows model‐based control approaches to be applied to systems with partially unknown dynamics. Gaussian process regression is a preferred approach that outputs not only the learned system model but also the variance of the model, which can be seen as a measure of uncertainty.
Daniel Landgraf   +2 more
wiley   +1 more source

Text Mining in Bibliometrics and Science Mapping: A Methodological Review

open access: yesWIREs Computational Statistics, Volume 18, Issue 2, June 2026.
Text mining has become a foundational component of contemporary bibliometrics and science mapping, enabling systematic analysis of the semantic structure, thematic evolution, and cognitive organization of scientific fields. Integrating textual evidence with relational indicators enriches knowledge maps and supports more comprehensive, content‐sensitive
Michelangelo Misuraca
wiley   +1 more source

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