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of 23
pro vyhledávání: '"Xenopoulos, Peter"'
Autor:
Solunke, Parikshit, Guardieiro, Vitoria, Rulff, Joao, Xenopoulos, Peter, Chan, Gromit Yeuk-Yin, Barr, Brian, Nonato, Luis Gustavo, Silva, Claudio
With the increasing use of black-box Machine Learning (ML) techniques in critical applications, there is a growing demand for methods that can provide transparency and accountability for model predictions. As a result, a large number of local explain
Externí odkaz:
http://arxiv.org/abs/2406.15613
Esports and high performance human-computer interaction are on the forefront of applying new hardware and software technologies in practice. Despite that, there is a paucity of research on how semi-professional and professional championship level pla
Externí odkaz:
http://arxiv.org/abs/2311.05424
Publikováno v:
Physical Culture and Sport: Studies and Research, Vol 105, Iss 1, Pp 13-23 (2024)
Esports and high-performance human-computer interaction are at the forefront of implementing new hardware and software technologies Despite that, there is a paucity of research on how championship-level esports professionals approach aspects of their
Externí odkaz:
https://doaj.org/article/f9478beb63bd4d42b40d7dc316d0be11
Massively multiplayer online role-playing games often contain sophisticated in-game economies. Many important real-world economic phenomena, such as inflation, economic growth, and business cycles, are also present in these virtual economies. One maj
Externí odkaz:
http://arxiv.org/abs/2210.07970
Autor:
Xenopoulos, Peter, Silva, Claudio
Sports, due to their global reach and impact-rich prediction tasks, are an exciting domain to deploy machine learning models. However, data from conventional sports is often unsuitable for research use due to its size, veracity, and accessibility. To
Externí odkaz:
http://arxiv.org/abs/2209.09861
Autor:
Xenopoulos, Peter, Silva, Claudio
Predicting outcomes in sports is important for teams, leagues, bettors, media, and fans. Given the growing amount of player tracking data, sports analytics models are increasingly utilizing spatially-derived features built upon player tracking data.
Externí odkaz:
http://arxiv.org/abs/2207.14124
Analyzing classification model performance is a crucial task for machine learning practitioners. While practitioners often use count-based metrics derived from confusion matrices, like accuracy, many applications, such as weather prediction, sports b
Externí odkaz:
http://arxiv.org/abs/2207.13770
Autor:
Xenopoulos, Peter, Chan, Gromit, Doraiswamy, Harish, Nonato, Luis Gustavo, Barr, Brian, Silva, Claudio
Local explainability methods -- those which seek to generate an explanation for each prediction -- are becoming increasingly prevalent due to the need for practitioners to rationalize their model outputs. However, comparing local explainability metho
Externí odkaz:
http://arxiv.org/abs/2201.02155
The outputs of win probability models are often used to evaluate player actions. However, in some sports, such as the popular esport Counter-Strike, there exist important team-level decisions. For example, at the beginning of each round in a Counter-
Externí odkaz:
http://arxiv.org/abs/2109.12990
While esports organizations are increasingly adopting practices of conventional sports teams, such as dedicated analysts and data-driven decision-making, video-based game review is still the primary mode of game analysis. In conventional sports, adva
Externí odkaz:
http://arxiv.org/abs/2107.06495