Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Urbanowicz, Ryan J."'
Publikováno v:
J. Romero et al. (Eds.), EvoMUSART 2020, LNCS 12103, pp. 165-178, 2020
We have recently developed OMNIREP, a coevolutionary algorithm to discover both a representation and an interpreter that solve a particular problem of interest. Herein, we demonstrate that the OMNIREP framework can be successfully applied within the
Externí odkaz:
http://arxiv.org/abs/2401.11167
Publikováno v:
W. Banzhaf et al. (eds.), Genetic Programming Theory and Practice XVII, Genetic and Evolutionary Computation, 2020
The simultaneous evolution of two or more species with coupled fitness -- coevolution -- has been put to good use in the field of evolutionary computation. Herein, we present two new forms of coevolutionary algorithms, which we have recently designed
Externí odkaz:
http://arxiv.org/abs/2401.10515
Autor:
Urbanowicz, Ryan J., Bandhey, Harsh, Keenan, Brendan T., Maislin, Greg, Hwang, Sy, Mowery, Danielle L., Lynch, Shannon M., Mazzotti, Diego R., Han, Fang, Li, Qing Yun, Penzel, Thomas, Tufik, Sergio, Bittencourt, Lia, Gislason, Thorarinn, de Chazal, Philip, Singh, Bhajan, McArdle, Nigel, Chen, Ning-Hung, Pack, Allan, Schwab, Richard J., Cistulli, Peter A., Magalang, Ulysses J.
While machine learning (ML) includes a valuable array of tools for analyzing biomedical data, significant time and expertise is required to assemble effective, rigorous, and unbiased pipelines. Automated ML (AutoML) tools seek to facilitate ML applic
Externí odkaz:
http://arxiv.org/abs/2312.05461
When seeking a predictive model in biomedical data, one often has more than a single objective in mind, e.g., attaining both high accuracy and low complexity (to promote interpretability). We investigate herein whether multiple objectives can be dyna
Externí odkaz:
http://arxiv.org/abs/2206.15409
Publikováno v:
EuroGP 2019, LNCS 11451, pages 1-16, 2019
We recently highlighted a fundamental problem recognized to confound algorithmic optimization, namely, \textit{conflating} the objective with the objective function. Even when the former is well defined, the latter may not be obvious, e.g., in learni
Externí odkaz:
http://arxiv.org/abs/2206.12707
Publikováno v:
Proceedings of 2019 IEEE Congress on Evolutionary Computation
We have recently presented SAFE -- Solution And Fitness Evolution -- a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate solutions and a population of candidate objective functions. We showed
Externí odkaz:
http://arxiv.org/abs/2206.13509
Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex associations. Many useful tools/packages (e.g. scikit-learn) have been developed to make the various elements of
Externí odkaz:
http://arxiv.org/abs/2206.12002
Machine learning (ML) research has yielded powerful tools for training accurate prediction models despite complex multivariate associations (e.g. interactions and heterogeneity). In fields such as medicine, improved interpretability of ML modeling is
Externí odkaz:
http://arxiv.org/abs/2104.12844
Autor:
Urbanowicz, Ryan J., Suri, Pranshu, Cui, Yuhan, Moore, Jason H., Ruth, Karen, Stolzenberg-Solomon, Rachael, Lynch, Shannon M.
Machine learning (ML) offers a collection of powerful approaches for detecting and modeling associations, often applied to data having a large number of features and/or complex associations. Currently, there are many tools to facilitate implementing
Externí odkaz:
http://arxiv.org/abs/2008.12829
Autor:
Blette, Bryan S, Moutchia, Jude, Al-Naamani, Nadine, Ventetuolo, Corey E, Cheng, Chao, Appleby, Dina, Urbanowicz, Ryan J, Fritz, Jason, Mazurek, Jeremy A, Li, Fan, Kawut, Steven M, Harhay, Michael O *
Publikováno v:
In The Lancet Respiratory Medicine October 2023 11(10):873-882