Nikolaos Nikolaou

According to our database, Nikolaos Nikolaou authored at least 14 papers between 2014 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Fast regression of the tritium breeding ratio in fusion reactors.

Petr Mánek, Graham W. Van Goffrier, Vignesh Gopakumar, Nikolaos Nikolaou, Jonathan Shimwell, Ingo P. Waldmann

Mach. Learn. Sci. Technol., March, 2023

2021

Lessons Learned from Ariel Data Challenge 2022 - Inferring Physical Properties of Exoplanets From Next-Generation Telescopes.

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

Proceedings of the NeurIPS 2022 Competition Track, 2021

2020

Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals.

Kai Hou Yip, Quentin Changeat, Nikolaos Nikolaou, Mario Morvan, Billy Edwards, Ingo P. Waldmann, Giovanna Tinetti

CoRR, 2020

PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch.

Mario Morvan, Angelos Tsiaras, Nikolaos Nikolaou, Ingo P. Waldmann

CoRR, 2020

Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots.

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

CoRR, 2020

Inferring Causal Direction from Observational Data: A Complexity Approach.

Nikolaos Nikolaou, Konstantinos Sechidis

CoRR, 2020

Margin Maximization as Lossless Maximal Compression.

Nikolaos Nikolaou, Henry W. J. Reeve, Gavin Brown

CoRR, 2020

Better Boosting with Bandits for Online Learning.

Nikolaos Nikolaou, Joseph C. Mellor, Nikunj C. Oza, Gavin Brown

CoRR, 2020

2019

Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning.

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

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

2017

Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions.

Nikolaos Nikolaou, Efstratios Batzelis, Gavin Brown

Proceedings of the Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy, 2017

2016

Cost-sensitive boosting: a unified approach.

Nikolaos Nikolaou

PhD thesis, 2016

Cost-sensitive boosting algorithms: Do we really need them?

Nikolaos Nikolaou, Narayanan Unny Edakunni, Meelis Kull, Peter A. Flach, Gavin Brown

Mach. Learn., 2016

2015

Calibrating AdaBoost for Asymmetric Learning.

Nikolaos Nikolaou, Gavin Brown

Proceedings of the Multiple Classifier Systems - 12th International Workshop, 2015

2014

Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood.

Konstantinos Sechidis, Nikolaos Nikolaou, Gavin Brown

Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2014