Results 21 to 30 of about 436 (146)
Financial Time Series Uncertainty: A Review of Probabilistic AI Applications
ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen +4 more
wiley +1 more source
Uncertainties in stochastic programming models: The minimax approach [PDF]
50 years ago, stochastic programming was introduced to deal with uncertain values of coefficients which were observed in applications of mathematical programming.
Dupacová, Jitka
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Generative Models for Crystalline Materials
Generative machine learning models are increasingly used in crystalline materials design. This review outlines major generative approaches and assesses their strengths and limitations. It also examines how generative models can be adapted to practical applications, discusses key experimental considerations for evaluating generated structures, and ...
Houssam Metni +15 more
wiley +1 more source
Minimax and applications [PDF]
Techniques and principles of minimax theory play a key role in many areas of research, including game theory, optimization, and computational complexity.
Du, Ding-Zhu, Pardalos, Panos
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We present Diffusion‐MRI‐based Estimation of Cortical Architecture via Machine Learning (DECAM), a deep‐learning framework for estimating primate brain cortical architecture optimized with best response constraint and cortical label vectors. Trained using macaque brain high‐resolution multi‐shell dMRI and histology data, DECAM generates high‐fidelity ...
Tianjia Zhu +7 more
wiley +1 more source
Recanting Twins: Addressing Intermediate Confounding in Mediation Analysis
ABSTRACT The presence of intermediate confounders, also called recanting witnesses, is a fundamental challenge to the investigation of causal mechanisms in mediation analysis, preventing the identification of natural path‐specific effects. Common alternatives (such as randomizational interventional effects) are problematic because they can take non ...
Tat‐Thang Vo +4 more
wiley +1 more source
Duality theory in fuzzy mathematical programming problems with fuzzy coefficients [PDF]
In this paper, the notions of subgradient, subdifferential, and differential with respect to convex fuzzy mappings are investigated, which provides the basis for the fuzzy extremum problem theory.
E. Stanley Lee +5 more
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Maintaining reliable tracking control in networked control systems over fading wireless channels is difficult due to the stochastic and time‐correlated nature of wireless links. This work proposes a transition‐aware Q‐learning (TA‐QL) method that learns robust control policies directly from networked data without requiring explicit models, while ...
Ehsan Badfar, Babak Tavassoli
wiley +1 more source
Varying confidence levels for CVaR risk measures and minimax limits [PDF]
Conditional value at risk (CVaR) has been widely studied as a risk measure. In this paper we add to this work by focusing on the choice of confidence level and its impact on optimization problems with CVaR appearing in the objective and also the ...
Xu, Huifu, Anderson, Edward, Zhang, Dali
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ABSTRACT To enhance the techno‐economic performance and robustness of multi‐microgrids (MMG) systems, this paper proposes a two‐stage bi‐level collaborative optimisation strategy integrating energy sharing and price incentives. In the day‐ahead stage, the shared energy storage operator (SESO) at the upper level employs conditional Wasserstein ...
Xianghu Cui +4 more
wiley +1 more source

