| Cost-sensitive boosting algorithms: Do we really need them? N Nikolaou, N Edakunni, M Kull, P Flach, G Brown Machine Learning 104 (2), 359-384, 2016 | 42 | 2016 |
| Information theoretic feature selection in multi-label data through composite likelihood K Sechidis, N Nikolaou, G Brown Joint IAPR International Workshops on Statistical Techniques in Pattern …, 2014 | 19 | 2014 |
| Calibrating AdaBoost for asymmetric learning N Nikolaou, G Brown International Workshop on Multiple Classifier Systems, 112-124, 2015 | 7 | 2015 |
| Detrending Exoplanetary Transit Light Curves with Long Short-term Memory Networks M Morvan, N Nikolaou, A Tsiaras, IP Waldmann The Astronomical Journal 159 (3), 109, 2020 | 5 | 2020 |
| Cost-Sensitive Boosting: A Unified Approach N Nikolaou University of Manchester, 2016 | 5 | 2016 |
| Music emotion classification N Nikolaou Dissertation for the Diploma of Electronic and Computer Engineering …, 2011 | 5 | 2011 |
| Margin Maximization as Lossless Maximal Compression N Nikolaou, H Reeve, G Brown arXiv preprint arXiv:2001.10318, 2020 | 2 | 2020 |
| Fast optimization of non-convex Machine Learning objectives N Nikolaou M.Sc. Dissertation, 2012 | 2 | 2012 |
| Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning KH Yip, N Nikolaou, P Coronica, A Tsiaras, B Edwards, Q Changeat, ... Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019 | 1 | 2019 |
| Fast Regression of the Tritium Breeding Ratio in Fusion Reactors P Mánek, G Van Goffrier, V Gopakumar, N Nikolaou, J Shimwell, ... arXiv preprint arXiv:2104.04026, 2021 | | 2021 |
| PyLightcurve-torch: a transit modeling package for deep learning applications in PyTorch M Morvan, A Tsiaras, N Nikolaou, IP Waldmann Publications of the Astronomical Society of the Pacific 133 (1021), 034505, 2021 | | 2021 |
| Lossless Compression and Generalization in Overparameterized Models: The Case of Boosting N Nikolaou Neural Compression: From Information Theory to Applications--Workshop@ ICLR 2021, 2021 | | 2021 |
| Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals KH Yip, Q Changeat, N Nikolaou, M Morvan, B Edwards, IP Waldmann, ... arXiv preprint arXiv:2011.11284, 2020 | | 2020 |
| Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots N Nikolaou, IP Waldmann, A Tsiaras, M Morvan, B Edwards, KH Yip, ... arXiv preprint arXiv:2010.15996, 2020 | | 2020 |
| Inferring Causal Direction from Observational Data: A Complexity Approach N Nikolaou, K Sechidis arXiv preprint arXiv:2010.05635, 2020 | | 2020 |
| A Deep Learning Pipeline for Unified Modelling of Time-Correlated Noise in Exoplanets Observations M Morvan, N Nikolau, A Tsiaras, I Waldmann European Planetary Science Congress, EPSC2020-373, 2020 | | 2020 |
| Mapping Mineralogical Distributions on Mars with Unsupervised Machine Learning M Hipperson, I Waldmann, P Grindrod, N Nikolaou European Planetary Science Congress, EPSC2020-773, 2020 | | 2020 |
| Better Boosting with Bandits for Online Learning N Nikolaou, J Mellor, NC Oza, G Brown arXiv preprint arXiv:2001.06105, 2020 | | 2020 |
| Correcting Transiting Exoplanet Light Curves for Stellar Spots: A Machine Learning Challenge for the ESA Ariel Space Mission N Nikolaou, I Waldmann, S Sarkar, A Tsiaras, B Edwards, M Morvan, ... AAS/Division for Extreme Solar Systems Abstracts 51, 330.07, 2019 | | 2019 |
| Gradient boosting models for photovoltaic power estimation under partial shading conditions N Nikolaou, E Batzelis, G Brown International Workshop on Data Analytics for Renewable Energy Integration, 13-25, 2017 | | 2017 |
Gavin BrownProfessor of Machine Learning, Dept of Computer Science, University of ManchesterVerified email at manchester.ac.uk
