G
Gilles Pagès
Researcher at French Institute of Health and Medical Research
Publications - 403
Citations - 25339
Gilles Pagès is an academic researcher from French Institute of Health and Medical Research. The author has contributed to research in topics: Quantization (signal processing) & MAPK/ERK pathway. The author has an hindex of 73, co-authored 398 publications receiving 22584 citations. Previous affiliations of Gilles Pagès include Paul Sabatier University & French Institute for Research in Computer Science and Automation.
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CVaR HEDGING USING QUANTIZATION‐BASED STOCHASTIC APPROXIMATION ALGORITHM
TL;DR: A method based on risk minimization to hedge observable but nontradable source of risk on financial or energy markets using three main tools: a stochastic approximation algorithm, optimal quantization, and variance reduction techniques, as the quantities of interest are naturally related to rare events.
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Insulin-Like Growth Factor-I Activates Extracellularly Regulated Kinase to Regulate the P450 Side-Chain Cleavage Insulin-Like Response Element in Granulosa Cells
TL;DR: Investigating IGF-stimulated ERK signaling regulating P450scc gene expression in the immortalized porcine granulosa cell line JC-410 found that phosphorylated ERK (pERK) bound PSF under basal conditions and chromatin immunoprecipitation analysis showed that PSF and Sp1 constitutively occupy the P 450scc promoter independent of IGF-I treatment.
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A mixed-step algorithm for the approximation of the stationary regime of a diffusion
Gilles Pagès,Fabien Panloup +1 more
TL;DR: In this paper, a mixed-step Euler scheme is proposed to approximate the stationary regime of a diffusion (possibly with jumps) for a class of functionals of the process.
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From Malignant Progression to Therapeutic Targeting: Current Insights of Mesothelin in Pancreatic Ductal Adenocarcinoma.
TL;DR: A general overview of the different roles sustained by MSLN during PDAC progression is provided and the different MSLn-targeted therapies that are currently tested in the clinic are summarized.
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Limit theorems for weighted and regular Multilevel estimators
TL;DR: In this paper, the convergence and weak rate of the Multilevel Monte Carlo estimator (MLMC) and its weighted version (ML2R) were analyzed in terms of a Strong Law of Large Numbers and a Central Limit Theorem.