291 citations · 936 across the 29 of their papers we have counts for
33 papers
Fermi LAT AGN classification using supervised machine learning
Nathaniel Cooper, Maria Giovanna Dainotti, Aditya Narendra +2
Classifying Active Galactic Nuclei (AGN) is a challenge, especially for BL Lac Objects (BLLs), which are identified by their weak emission line spectra. To address the problem of c…
Reducing the uncertainty on the Hubble constant up to 35\% with an improved statistical analysis: different best-fit likelihoods for Supernovae Ia, Baryon Acoustic Oscillations, Quasars, and Gamma-Ray Bursts
Maria Giovanna Dainotti, Giada Bargiacchi, Małgorzata Bogdan +5
Cosmological models and their parameters are widely debated, especially about whether the current discrepancy between the values of the Hubble constant, , obtained by type I…
Weak pattern convergence for SLOPE and its robust versions
Ivan Hejný, Jonas Wallin, Małgorzata Bogdan
The Sorted L-One Estimator (SLOPE) is a popular regularization method in regression, which induces clustering of the estimated coefficients. That is, the estimator can have coeffic…
Reduced uncertainties up to 43\% on the Hubble constant and the matter density with the SNe Ia with a new statistical analysis
Maria Giovanna Dainotti, Giada Bargiacchi, Malgorzata Bogdan +2
Type Ia Supernovae (SNe Ia) are considered the most reliable \textit{standard candles} and they have played an invaluable role in cosmology since the discovery of the Universe's ac…
Sparse Graphical Modelling via the Sorted L-Norm
Riccardo Riccobello, Malgorzata Bogdan, Giovanni Bonaccolto +3
Sparse graphical modelling has attained widespread attention across various academic fields. We propose two new graphical model approaches, Gslope and Tslope, which provide sparse…
Pattern recovery by SLOPE
Małgorzata Bogdan, Xavier Dupuis, Piotr Graczyk +4
SLOPE is a popular method for dimensionality reduction in the high-dimensional regression. Indeed some regression coefficient estimates of SLOPE can be null (sparsity) or can be eq…