Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Sofia Triantafillou"'
Publikováno v:
PLoS ONE, Vol 17, Iss 11, p e0276755 (2022)
Treatments often come with thresholds, e.g. we are given statins if our cholesterol is above a certain threshold. But which statin administration threshold maximizes our quality of life adjusted years? More generally, which threshold would optimize t
Externí odkaz:
https://doaj.org/article/035bfe37540943b681eeadb46beb5770
Autor:
Sofia Triantafillou, Vincenzo Lagani, Christina Heinze-Deml, Angelika Schmidt, Jesper Tegner, Ioannis Tsamardinos
Publikováno v:
Scientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
Abstract Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods f
Externí odkaz:
https://doaj.org/article/ec82a6e1b2fd40ee8b1e4ad2d4552b47
Publikováno v:
IEEE transactions on neural networks and learning systems.
Causal discovery is continually being enriched with new algorithms for learning causal graphical probabilistic models. Each one of them requires a set of hyperparameters, creating a great number of combinations. Given that the true graph is unknown a
Autor:
Sofia Triantafillou, Greg Cooper
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:9940-9948
Estimating causal effects from observational data is not always possible due to confounding. Identifying a set of appropriate covariates (adjustment set) and adjusting for their influence can remove confounding bias; however, such a set is often not
Autor:
Bernard ’t Hart, Titipat Achakulvisut, Ayoade Adeyemi, Athena Akrami, Bradly Alicea, Alicia Alonso-Andres, Diego Alzate-Correa, Arash Ash, Jesus Ballesteros, Aishwarya Balwani, Eleanor Batty, Ulrik Beierholm, Ari Benjamin, Upinder Bhalla, Gunnar Blohm, Joachim Blohm, Kathryn Bonnen, Marco Brigham, Bingni Brunton, John Butler, Brandon Caie, N Gajic, Sharbatanu Chatterjee, Spyridon Chavlis, Ruidong Chen, You Cheng, H.m. Chow, Raymond Chua, Yunwei Dai, Isaac David, Eric DeWitt, Julien Denis, Alish Dipani, Arianna Dorschel, Jan Drugowitsch, Kshitij Dwivedi, Sean Escola, Haoxue Fan, Roozbeh Farhoodi, Yicheng Fei, Pierre-Étienne Fiquet, Lorenzo Fontolan, Jeremy Forest, Yuki Fujishima, Byron Galbraith, Mario Galdamez, Richard Gao, Julijana Gjorgjieva, Alexander Gonzalez, Qinglong Gu, Yueqi Guo, Ziyi Guo, Pankaj Gupta, Busra Gurbuz, Caroline Haimerl, Jordan Harrod, Alexandre Hyafil, Martin Irani, Daniel Jacobson, Michelle Johnson, Ilenna Jones, Gili Karni, Robert Kass, Hyosub Kim, Andreas Kist, Randal Koene, Konrad Kording, Matthew Krause, Arvind Kumar, Norma Kühn, Ray Lc, Matthew Laporte, Junseok Lee, Songting Li, Sikun Lin, Yang Lin, Shuze Liu, Tony Liu, Jesse Livezey, Linlin Lu, Jakob Macke, Kelly Mahaffy, A Martins, Nicolás Martorell, Manolo Martínez, Marcelo Mattar, Jorge Menendez, Kenneth Miller, Patrick Mineault, Nosratullah Mohammadi, Yalda Mohsenzadeh, Elenor Morgenroth, Taha Morshedzadeh, Alice Mosberger, Madhuvanthi Muliya, Marieke Mur, John Murray, Yashas Nd, Richard Naud, Prakriti Nayak, Anushka Oak, Itzel Castillo, Seyedmehdi Orouji, Jorge Otero-Millan, Marius Pachitariu, Biraj Pandey, Renato Paredes, Jesse Parent, Il Park, Megan Peters, Xaq Pitkow, Panayiota Poirazi, Haroon Popal, Sandhya Prabhakaran, Tian Qiu, Srinidhi Ragunathan, Raul Rodriguez-Cruces, David Rolnick, Ashish Sahoo, Saeed Salehinajafabadi, Cristina Savin, Shreya Saxena, Paul Schrater, Karen Schroeder, Alice Schwarze, Madineh Sedigh-Sarvestani, K Sekhar, Reza Shadmehr, Maryam Shanechi, Siddhant Sharma, Eric Shea-Brown, Krishna Shenoy, Carolina Shimabukuro, Sergey Shuvaev, Man Sin, Maurice Smith, Nicholas Steinmetz, Karolina Stosio, Elizabeth Straley, Gabrielle Strandquist, Carsen Stringer, Rimjhim Tomar, Ngoc Tran, Sofia Triantafillou, Lawrence Udeigwe, Davide Valeriani, Vincent Valton, Maryam Vaziri-Pashkam, Peter Vincent, Gal Vishne, Pascal Wallisch, Peiyuan Wang, Claire Ward, Michael Waskom, Kunlin Wei, Anqi Wu, Zhengwei Wu, Brad Wyble, Lei Zhang, Daniel Zysman, Federico Uquillas, Tara van Viegen
Publikováno v:
Journal of Open Source Education, 2022, Vol.5(49), pp.118 [Peer Reviewed Journal]
Articles
Articles
Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscien
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ee962b80a3fba55cd5a9118ae3ca495
http://dro.dur.ac.uk/37609/
http://dro.dur.ac.uk/37609/
Publikováno v:
Med Image Comput Comput Assist Interv
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030871987
MICCAI (3)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030871987
MICCAI (3)
Model explainability is essential for the creation of trustworthy Machine Learning models in healthcare. An ideal explanation resembles the decision-making process of a domain expert and is expressed using concepts or terminology that is meaningful t
Autor:
Sofia Triantafillou
Η επαναλαμβανόμενη μελέτη ενός συστήματος υπό διαφορετικές οπτικές για την εξαγωγή ενός συμπεράσματος είναι συχνό φαινόμενο στην επισ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c20ca41a7f6fad5c447c67f8f186fa14
https://doi.org/10.12681/eadd/36134
https://doi.org/10.12681/eadd/36134
Autor:
Sofia Triantafillou, Judit Marsillach, Gary M. Shaw, Jeanette A. Stingone, Alexandra Larsen, Jay P. Kitt
Publikováno v:
Environ Res
Rates of preterm birth and low birthweight continue to rise in the United States and pose a significant public health problem. Although a variety of environmental exposures are known to contribute to these and other adverse birth outcomes, there has
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03adf337afeb165fb497eee15e7ffe16
https://europepmc.org/articles/PMC8187296/
https://europepmc.org/articles/PMC8187296/
Publikováno v:
International Journal of Approximate Reasoning. 102:74-85
We consider the problem of causal structure learning in presence of latent confounders. We propose a hybrid method, MAG Max–Min Hill-Climbing (M3HC) that takes as input a data set of continuous variables, assumed to follow a multivariate Gaussian d
Autor:
Ioannis Tsamardinos, Sofia Triantafillou, Vincenzo Lagani, Jesper Tegnér, Christina Heinze-Deml, Angelika Schmidt
Publikováno v:
Scientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
Scientific Reports, 7 (1)
Scientific Reports
Scientific Reports, 7 (1)
Scientific Reports
Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automa