Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood. - Konstantinos Sechidis; Nikolaos Nikolaou; Gavin Brown 001 (S+SSPR, 2014)

Calibrating AdaBoost for Asymmetric Learning. - Nikolaos Nikolaou; Gavin Brown 001 (MCS, 2015)

Cost-sensitive boosting algorithms: Do we really need them? - Nikolaos Nikolaou; Narayanan Unny Edakunni; Meelis Kull; Peter A. Flach; Gavin Brown 001 (Mach. Learn., 2016)

Cost-sensitive boosting: a unified approach. - Nikolaos Nikolaou (2016)

Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions. - Nikolaos Nikolaou; Efstratios Batzelis; Gavin Brown 001 (DARE@PKDD/ECML, 2017)

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; et al. (ECML/PKDD (3), 2019)

Better Boosting with Bandits for Online Learning. - Nikolaos Nikolaou; Joseph C. Mellor; Nikunj C. Oza; Gavin Brown 001 (CoRR, 2020)

Margin Maximization as Lossless Maximal Compression. - Nikolaos Nikolaou; Henry W. J. Reeve; Gavin Brown 001 (CoRR, 2020)

Inferring Causal Direction from Observational Data: A Complexity Approach. - Nikolaos Nikolaou; Konstantinos Sechidis (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; et al. (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)

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)

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; et al. (NeurIPS (Competition and Demos), 2021)

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., 2023)

AGM-TE: Approximate Generative Model Estimator of Transfer Entropy for Causal Discovery. - Daniel Kornai; Ricardo Silva; Nikolaos Nikolaou (CLeaR, 2025)

Physics-Based Tool Usage Simulations in VR. - Nikolaos Partarakis; Xenophon Zabulis; Dimitris Zourarakis; Ioanna Demeridou; Ines Moreno; Arnaud Dubois; Nikolaos Nikolaou; Peiman Fallahian; David Arnaud; Noël Crescenzo; Patricia Hee; Andriani Stamou (Multimodal Technol. Interact., 2025)

Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach. - R. P. Nathan; Nikolaos Nikolaou; Ofer Lahav (CoRR, 2025)

Extreme Learning Machines for Exoplanet Simulations: A Faster, Lightweight Alternative to Deep Learning. - Tara P. A. Tahseen; Luís F. Simões; Kai Hou Yip; Nikolaos Nikolaou; João M. Mendonça; Ingo P. Waldmann (CoRR, 2025)