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ORCIDNikolaos Nikolaou
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- affiliation: The University of Manchester, UK
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2020 – today
- 2023
[j2]Petr Mánek
, Graham W. Van Goffrier, Vignesh Gopakumar, Nikolaos Nikolaou, Jonathan Shimwell, Ingo P. Waldmann:
Fast regression of the tritium breeding ratio in fusion reactors. Mach. Learn. Sci. Technol. 4(1): 15008 (2023)- 2021
- [c5]Kai Hou Yip, Quentin Changeat, Ingo P. Waldmann, Eyup B. Unlu, Roy T. Forestano, Alexander Roman, Katia Matcheva, Konstantin T. Matchev, Stefan Stefanov, Ondrej Podsztavek, Mario Morvan, Nikolaos Nikolaou, Ahmed F. Al-Refaie, Clare Jenner, Chris Johnson, Angelos Tsiaras, Billy Edwards, Catarina Alves de Oliveira, Jeyan Thiyagalingam, Pierre-Olivier Lagage, James Cho, Giovanna Tinetti:
Lessons Learned from Ariel Data Challenge 2022 - Inferring Physical Properties of Exoplanets From Next-Generation Telescopes. NeurIPS (Competition and Demos) 2021: 1-17 - [i7]Petr Mánek, Graham W. Van Goffrier, Vignesh Gopakumar, Nikolaos Nikolaou, Jonathan Shimwell, Ingo P. Waldmann:
Fast Regression of the Tritium Breeding Ratio in Fusion Reactors. CoRR abs/2104.04026 (2021) - 2020
- [i6]Nikolaos Nikolaou, Joseph C. Mellor, Nikunj C. Oza, Gavin Brown:
Better Boosting with Bandits for Online Learning. CoRR abs/2001.06105 (2020) - [i5]Nikolaos Nikolaou, Henry W. J. Reeve, Gavin Brown:
Margin Maximization as Lossless Maximal Compression. CoRR abs/2001.10318 (2020) - [i4]Nikolaos Nikolaou, Konstantinos Sechidis:
Inferring Causal Direction from Observational Data: A Complexity Approach. CoRR abs/2010.05635 (2020) - [i3]Nikolaos Nikolaou, Ingo P. Waldmann, Angelos Tsiaras, Mario Morvan, Billy Edwards, Kai Hou Yip, Giovanna Tinetti, Subhajit Sarkar, James M. Dawson, Vadim Borisov, Gjergji Kasneci, Matej Petkovic, Tomaz Stepisnik, Tarek Al-Ubaidi, Rachel Louise Bailey, Michael Granitzer, Sahib Julka, Roman Kern, Patrick Ofner, Stefan Wagner, Lukas Heppe, Mirko Bunse, Katharina Morik:
Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots. CoRR abs/2010.15996 (2020) - [i2]Mario Morvan, Angelos Tsiaras, Nikolaos Nikolaou, Ingo P. Waldmann:
PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch. CoRR abs/2011.02030 (2020) - [i1]Kai Hou Yip, Quentin Changeat, Nikolaos Nikolaou, Mario Morvan, Billy Edwards, Ingo P. Waldmann, Giovanna Tinetti:
Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals. CoRR abs/2011.11284 (2020)
2010 – 2019
- 2019
- [c4]Kai Hou Yip, Nikolaos Nikolaou, Piero Coronica, Angelos Tsiaras, Billy Edwards, Quentin Changeat, Mario Morvan, Beth Biller, Sasha Hinkley, Jeffrey Salmond, Matthew Archer, Paul Sumption, Elodie Choquet, Remi Soummer, Laurent Pueyo, Ingo P. Waldmann:
Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning. ECML/PKDD (3) 2019: 322-338 - 2017
- [c3]Nikolaos Nikolaou, Efstratios Batzelis, Gavin Brown:
Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions. DARE@PKDD/ECML 2017: 13-25 - 2016
- [b1]Nikolaos Nikolaou:
Cost-sensitive boosting: a unified approach. University of Manchester, UK, 2016 - [j1]Nikolaos Nikolaou, Narayanan Unny Edakunni, Meelis Kull, Peter A. Flach, Gavin Brown:
Cost-sensitive boosting algorithms: Do we really need them? Mach. Learn. 104(2-3): 359-384 (2016) - 2015
- [c2]Nikolaos Nikolaou, Gavin Brown:
Calibrating AdaBoost for Asymmetric Learning. MCS 2015: 112-124 - 2014
- [c1]Konstantinos Sechidis, Nikolaos Nikolaou, Gavin Brown:
Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood. S+SSPR 2014: 143-152
Coauthor Index
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