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- 2024
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Mark
Gamma-Ray Bursts as Distance Indicators by a Statistical Learning Approach
(
- Contribution to journal › Article
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Mark
Inferring the Redshift of More than 150 GRBs with a Machine-learning Ensemble Model
(
- Contribution to journal › Article
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Mark
On the statistical assumption on the distance moduli of Supernovae Ia and its impact on the determination of cosmological parameters
(
- Contribution to journal › Article
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Mark
Shedding new light on the Hubble constant tension through Supernovae Ia
2024) 14th Frascati Workshops on Multifrequency Behaviour of High Energy Cosmic Sources, MULTIF 2023 447.(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
- 2023
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Mark
Fermi LAT AGN classification using supervised machine learning
(
- Contribution to journal › Article
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Mark
A power analysis for model-X knockoffs with ℓ p -regularized statistics
(
- Contribution to journal › Article
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Mark
Reducing the Uncertainty on the Hubble Constant up to 35% with an Improved Statistical Analysis : Different Best-fit Likelihoods for Type Ia Supernovae, Baryon Acoustic Oscillations, Quasars, and Gamma-Ray Bursts
(
- Contribution to journal › Article
- 2022
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Mark
Selecting predictive biomarkers from genomic data
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- Contribution to journal › Article
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Mark
Using Multivariate Imputation by Chained Equations to Predict Redshifts of Active Galactic Nuclei
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- Contribution to journal › Article
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Mark
Predicting the Redshift of Gamma-Ray Loud AGNs Using Supervised Machine Learning. II
(
- Contribution to journal › Article