A Mixed-Categorical Correlation Kernel for Gaussian Process

Saves, Paul; Diouane, Youssef; Bartoli, Nathalie; Lefèbvre, Thierry; Morlier, Joseph

doi:10.2139/ssrn.4296794

Cited by 7 publications

(25 citation statements)

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“…: Here X denotes the set of already observed study design vectors, K XX denotes the pairwise kernel matrix of our X vectors, and k ( x * , X ) denotes a column vector of kernels of x * with each element in X . Traditional Gaussian process models can be extended to mixed variables by adapting the kernel distance function (Saves et al ., 2023a). Generally the kernel function is factored into continuous and discrete kernels, with the overall kernel/similarity defined as their product.…”

Section: Methodsmentioning

confidence: 99%

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Optimizing Design of Genomics Studies for Clonal Evolution Analysis

Srivatsa,

Schwartz

2024

Preprint

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Genomic biotechnologies have seen rapid development over the past two decades, allowing for both the inference and modification of genetic and epigenetic information at the single cell level. While these tools present enormous potential for basic research, diagnostics, and treatment, they also raise difficult issues of how to design research studies to deploy these tools most effectively. In designing a study at the population or individual level, a researcher might combine several different sequencing modalities and sampling protocols, each with different utility, costs, and other tradeoffs. The central problem this paper attempts to address is then how one might create an optimal study design for a genomic analysis, with particular focus on studies involving somatic variation, typically for applications in cancer genomics. We pose the study design problem as a stochastic constrained nonlinear optimization problem and introduce a simulation-centered optimization procedure that iteratively optimizes the objective function using surrogate modeling combined with pattern and gradient search. Finally, we demonstrate the use of our procedure on diverse test cases to derive resource and study design allocations optimized for various objectives for the study of somatic cell populations.

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Section: Methodsmentioning

confidence: 99%

“…Generally the kernel function is factored into continuous and discrete kernels, with the overall kernel/similarity defined as their product. We use the exponential homoscedastic hypersphere kernel for categorical variables (c.f., Saves et al . (2023a),Garrido-Merchán and Hernández-Lobato (2020)).…”

Section: Methodsmentioning

confidence: 99%

Optimizing Design of Genomics Studies for Clonal Evolution Analysis

Srivatsa,

Schwartz

2024

Preprint

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“…Sampling was performed using the Latin Hypercube, a quasi-random method common in building applications of SMs [46]. The model training adopted the Kriging interpolation model, given its extensive strategies for dealing with mixed-integer problems [47].…”

Section: Figure 1 -Full Optimization Methodology In Terms Of Its Inpu...mentioning

confidence: 99%

Dynamic LCA methodology to support post-occupancy decision-making for carbon budget compliance

Mosquini,

Delinchant,

Jusselme

2024

Energy and Buildings

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“…13, showing how architecture elements and associated design parameters are mapped to disciplinary analysis tools. The optimization problem has been executed using SEGOMOE (Bartoli et al 2016), a surrogatebased optimization algorithm which has recently been extended to also support multi-objective (Grapin et al 2022) and mixed-integer (Saves et al 2022) problems. Access to the optimization algorithm is provided by WhatsOpt (Lafage, Defoort & Lefebvre 2019) through an ask-tell interface.…”

Section: Showcase: Designing a Family Of Business Jetsmentioning

confidence: 99%

The AGILE 4.0 Project: MBSE to Support Cyber‐Physical Collaborative Aircraft Development

Bussemaker,

Boggero,

Nagel

2023

INCOSE International Symp

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This paper presents the main output of the EU‐funded H2020 AGILE 4.0 project: the AGILE 4.0 MBSE‐MDAO Development Framework. The framework aims at connecting upstream Model‐Based Systems Engineering (MBSE) activities to downstream Multidisciplinary Design Analysis and Optimization (MDAO) activities, applied to the development of complex aeronautical systems. Upstream activities include the definition of stakeholders, needs, semantic requirements, and modeling the system architecture design space. Downstream activities include defining, implementing, and executing cross‐organizational and collaborative MDAO workflows to support system synthesis. The development framework is implemented in an Operational Collaborative Environment (OCE), a web‐based platform for project data and modeling tool access. A business jet family design application case demonstrates the use of the development framework and OCE.

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A Mixed-Categorical Correlation Kernel for Gaussian Process

Cited by 7 publications

References 0 publications

Optimizing Design of Genomics Studies for Clonal Evolution Analysis

Optimizing Design of Genomics Studies for Clonal Evolution Analysis

Dynamic LCA methodology to support post-occupancy decision-making for carbon budget compliance

The AGILE 4.0 Project: MBSE to Support Cyber‐Physical Collaborative Aircraft Development

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