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pro vyhledávání: '"Žliobaitė, Indre"'
Autor:
Žliobaitė, Indrė
This extended abstract was presented at the Nectar Track of ECML PKDD 2024 in Vilnius, Lithuania. The content supplements a recently published paper "Laws of Macroevolutionary Expansion" in the Proceedings of the National Academy of Sciences (PNAS).
Externí odkaz:
http://arxiv.org/abs/2410.02838
Autor:
Zliobaite, Indre, Read, Jesse
Machine learning from data streams is an active and growing research area. Research on learning from streaming data typically makes strict assumptions linked to computational resource constraints, including requirements for stream mining algorithms t
Externí odkaz:
http://arxiv.org/abs/2310.19811
Autor:
Read, Jesse, Žliobaitė, Indrė
The literature on machine learning in the context of data streams is vast and growing. However, many of the defining assumptions regarding data-stream learning tasks are too strong to hold in practice, or are even contradictory such that they cannot
Externí odkaz:
http://arxiv.org/abs/2212.14720
Autor:
Kulmala, Liisa, Žliobaitė, Indre, Nikinmaa, Eero, Nöjd, Pekka, Kolari, Pasi, Kabiri Koupaei, Kourosh, Hollmén, Jaakko, Mäkinen, Harri
Publikováno v:
Silva Fennica, Vol 50, Iss 5 (2016)
Despite the numerous studies on year-to-year variation of tree growth, the physiological mechanisms controlling annual variation in growth are still not understood in detail. We studied the applicability of data-driven approach i.e. different regress
Externí odkaz:
https://doaj.org/article/300c53391c674da0b6b723c76f986c10
Publikováno v:
In Ecological Informatics September 2024 82
Autor:
Arranz, Sara G., Casanovas-Vilar, Isaac, Žliobaitė, Indrė, Abella, Juan, Angelone, Chiara, Azanza, Beatriz, Bernor, Raymond, Cirilli, Omar, DeMiguel, Daniel, Furió, Marc, Pandolfi, Luca, Robles, Josep M., Sánchez, Israel M., van den Hoek Ostende, Lars W., Alba, David M.
Publikováno v:
In Journal of Human Evolution December 2023 185
Autor:
Zliobaite, Indre
Algorithms learned from data are increasingly used for deciding many aspects in our life: from movies we see, to prices we pay, or medicine we get. Yet there is growing evidence that decision making by inappropriately trained algorithms may unintenti
Externí odkaz:
http://arxiv.org/abs/1708.00754
Autor:
Saarinen, Juha, Oksanen, Otto, Žliobaitė, Indrė, Fortelius, Mikael, DeMiguel, Daniel, Azanza, Beatriz, Bocherens, Hervé, Luzón, Carmen, Solano-García, José, Yravedra, José, Courtenay, Lloyd A., Blain, Hugues-Alexandre, Sánchez-Bandera, Christian, Serrano-Ramos, Alexia, Rodriguez-Alba, Juan José, Viranta, Suvi, Barsky, Deborah, Tallavaara, Miikka, Oms, Oriol, Agustí, Jordi, Ochando, Juan, Carrión, José S., Jiménez-Arenas, Juan Manuel
Publikováno v:
In Quaternary Science Reviews 15 September 2021 268
Autor:
Zliobaite, Indre, Tatti, Nikolaj
We show how to adjust the coefficient of determination ($R^2$) when used for measuring predictive accuracy via leave-one-out cross-validation.
Externí odkaz:
http://arxiv.org/abs/1605.01703
Autor:
Zliobaite, Indre
Nowadays, many decisions are made using predictive models built on historical data.Predictive models may systematically discriminate groups of people even if the computing process is fair and well-intentioned. Discrimination-aware data mining studies
Externí odkaz:
http://arxiv.org/abs/1511.00148