Zobrazeno 1 - 10
of 6 387
pro vyhledávání: '"A. A. KAPTEIN"'
Autor:
Provodin, Danil, Akker, Bram van den, Katsimerou, Christina, Kaptein, Maurits, Pechenizkiy, Mykola
In supervised machine learning, privileged information (PI) is information that is unavailable at inference, but is accessible during training time. Research on learning using privileged information (LUPI) aims to transfer the knowledge captured in P
Externí odkaz:
http://arxiv.org/abs/2408.14319
We present a new algorithm based on posterior sampling for learning in Constrained Markov Decision Processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achieves near-optimal regret bounds while being advantageous empirically c
Externí odkaz:
http://arxiv.org/abs/2405.19017
Optimal treatment rules can improve health outcomes on average by assigning a treatment associated with the most desirable outcome to each individual. Due to an unknown data generation mechanism, it is appealing to use flexible models to estimate the
Externí odkaz:
http://arxiv.org/abs/2311.11765
We present a new algorithm based on posterior sampling for learning in constrained Markov decision processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achieves near-optimal regret bounds while being advantageous empirically c
Externí odkaz:
http://arxiv.org/abs/2309.15737
Autor:
Chen, Hongyi, Kaptein, Maurits
In order to achieve unbiased and efficient estimators of causal effects from observational data, covariate selection for confounding adjustment becomes an important task in causal inference. Despite recent advancements in graphical criterion for cons
Externí odkaz:
http://arxiv.org/abs/2305.16908
Autor:
Chen, Hongyi, Kaptein, Maurits
We focus on the extension of bivariate causal learning methods into multivariate problem settings in a systematic manner via a novel framework. It is purposive to augment the scale to which bivariate causal discovery approaches can be applied since c
Externí odkaz:
http://arxiv.org/abs/2305.16904
Autor:
Yvonne E. Kaptein, MD, Babak Yasmeh, MD, Suhail Q. Allaqaband, MD, Yuting P. Chiang, MD, Ijaz A. Malik, MD, M. Eyman Mortada, MD, FHRS
Publikováno v:
HeartRhythm Case Reports, Vol 10, Iss 9, Pp 651-655 (2024)
Externí odkaz:
https://doaj.org/article/48ba9362af2f4d01bab8eadcf91cf97d
Publikováno v:
Neurological Research and Practice, Vol 6, Iss 1, Pp 1-6 (2024)
Abstract Background Individuals with Parkinson’s disease (PD) report a diminished perceived functional autonomy as their condition progresses. For those seeking emergency care, it is unknown whether the patient-physician relationship is instrumenta
Externí odkaz:
https://doaj.org/article/ee8c734611d44b729f29e0e7dd70227f
Autor:
Dominik Kiemel, Ann-Sophie Helene Kroell, Solène Denolly, Uta Haselmann, Jean-François Bonfanti, Jose Ignacio Andres, Brahma Ghosh, Peggy Geluykens, Suzanne J. F. Kaptein, Lucas Wilken, Pietro Scaturro, Johan Neyts, Marnix Van Loock, Olivia Goethals, Ralf Bartenschlager
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-20 (2024)
Abstract Dengue fever represents a significant medical and socio-economic burden in (sub)tropical regions, yet antivirals for treatment or prophylaxis are lacking. JNJ-A07 was described as highly active against the different genotypes within each ser
Externí odkaz:
https://doaj.org/article/e78ff1b130a7428d836ca61a6614a777
In medical, social, and behavioral research we often encounter datasets with a multilevel structure and multiple correlated dependent variables. These data are frequently collected from a study population that distinguishes several subpopulations wit
Externí odkaz:
http://arxiv.org/abs/2212.03473