Adaptive Bayesian SLOPE: Model Selection With Incomplete Data

Autor: Wei Jiang, Veronika Rockova, Julie Josse, Szymon Majewski, Błażej Miasojedow, Małgorzata Bogdan

Publikováno v: Journal of Computational and Graphical Statistics. 31:113-137

We consider the problem of variable selection in high-dimensional settings with missing observations among the covariates. To address this relatively understudied problem, we propose a new synergis...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::213592091dd25e51990d05a99b773107
https://doi.org/10.1080/10618600.2021.1963263

Author response for 'Masserstein: linear regression of mass spectra by optimal transport'

Autor: Grzegorz Skoraczynski, Szymon Majewski, Anna Gambin, Dirk Valkenborg, Michał Aleksander Ciach, Błażej Miasojedow, Michał Startek

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e1e88e899f095b56ce65f171a2d4edf2
https://doi.org/10.1002/rcm.8956/v2/response1

Controversies on the position of the mandibular foramen - review of the literature

Autor: Marcin, Lipski, Piotr, Pelka, Szymon, Majewski, Weronika, Lipska, Tomasz, Gladysz, Klaudia, Walocha, Grazyna, Wisniewska

Publikováno v: Folia medica Cracoviensia. 53(4)

Foramen of mandible is the most important point considering the Halsted anesthesia. Position of this foramen seems to be stable, however there are lots of controversies regarded to its position. Based on the current literature authors revised datas f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::06184f654fffbb664ce7ed3d501ea8ab
https://pubmed.ncbi.nlm.nih.gov/25556512

Boosting reinforcement learning with sparse and rare rewards using Fleming-Viot particle systems

Autor: Daniel Mastropietro, Szymon Majewski, Urtzi Ayesta, Matthieu Jonckheere

Publikováno v: HAL
à paraître
15th European Workshop on Reinforcement Learning (EWRL 2022)
15th European Workshop on Reinforcement Learning (EWRL 2022), Sep 2022, Milano, Italy

International audience; We consider reinforcement learning control problems under the average reward criterion in which non-zero rewards are both sparse and rare, that is, they occur in very few states and have a very small steady-state probability.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::7b5c752d5597bb695bf794e40481a8a5
https://ut3-toulouseinp.hal.science/hal-03772025