Nikolaos Nikolaou

19 papers Journal 12Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	AGM-TE: Approximate Generative Model Estimator of Transfer Entropy for Causal Discovery. CLeaR Daniel Kornai, Ricardo Silva, Nikolaos Nikolaou
2025	J	`jnl`	Extreme Learning Machines for Exoplanet Simulations: A Faster, Lightweight Alternative to Deep Learning. CoRR Tara P. A. Tahseen, Luís F. Simões, Kai Hou Yip, Nikolaos Nikolaou, João M. Mendonça, Ingo P. Waldmann
2025	J	`jnl`	Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach. CoRR R. P. Nathan, Nikolaos Nikolaou, Ofer Lahav
2025	J	`jnl`	Physics-Based Tool Usage Simulations in VR. Multimodal Technol. Interact. Nikolaos Partarakis, Xenophon Zabulis, Dimitris Zourarakis, Ioanna Demeridou, Ines Moreno, Arnaud Dubois, Nikolaos Nikolaou, Peiman Fallahian, David Arnaud, Noël Crescenzo, Patricia Hee, Andriani Stamou
2023	J	`jnl`	Fast regression of the tritium breeding ratio in fusion reactors. Mach. Learn. Sci. Technol. Petr Mánek, Graham W. Van Goffrier, Vignesh Gopakumar, Nikolaos Nikolaou, Jonathan Shimwell, Ingo P. Waldmann
2021	J	`jnl`	Fast Regression of the Tritium Breeding Ratio in Fusion Reactors. CoRR Petr Mánek, Graham W. Van Goffrier, Vignesh Gopakumar, Nikolaos Nikolaou, Jonathan Shimwell, Ingo P. Waldmann
2021	—	`conf`	Lessons Learned from Ariel Data Challenge 2022 - Inferring Physical Properties of Exoplanets From Next-Generation Telescopes. NeurIPS (Competition and Demos) 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
2020	J	`jnl`	Better Boosting with Bandits for Online Learning. CoRR Nikolaos Nikolaou, Joseph C. Mellor, Nikunj C. Oza, Gavin Brown
2020	J	`jnl`	Inferring Causal Direction from Observational Data: A Complexity Approach. CoRR Nikolaos Nikolaou, Konstantinos Sechidis
2020	J	`jnl`	Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots. CoRR 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
2020	J	`jnl`	Margin Maximization as Lossless Maximal Compression. CoRR Nikolaos Nikolaou, Henry W. J. Reeve, Gavin Brown
2020	J	`jnl`	Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals. CoRR Kai Hou Yip, Quentin Changeat, Nikolaos Nikolaou, Mario Morvan, Billy Edwards, Ingo P. Waldmann, Giovanna Tinetti
2020	J	`jnl`	PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch. CoRR Mario Morvan, Angelos Tsiaras, Nikolaos Nikolaou, Ingo P. Waldmann
2019	—	`conf`	Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning. ECML/PKDD (3) 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
2017	—	`conf`	Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions. DARE@PKDD/ECML Nikolaos Nikolaou, Efstratios Batzelis, Gavin Brown
2016	J	`jnl`	Cost-sensitive boosting algorithms: Do we really need them? Mach. Learn. Nikolaos Nikolaou, Narayanan Unny Edakunni, Meelis Kull, Peter A. Flach, Gavin Brown
2016	—	`—`	Cost-sensitive boosting: a unified approach. Nikolaos Nikolaou
2015	—	`conf`	Calibrating AdaBoost for Asymmetric Learning. MCS Nikolaos Nikolaou, Gavin Brown
2014	—	`conf`	Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood. S+SSPR Konstantinos Sechidis, Nikolaos Nikolaou, Gavin Brown