Nikolaos Nikolaou
Nikolaos Nikolaou
Senior Research Fellow, UCL
Verified email at ucl.ac.uk - Homepage
Title
Cited by
Cited by
Year
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
472016
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
192014
Calibrating AdaBoost for asymmetric learning
N Nikolaou, G Brown
International Workshop on Multiple Classifier Systems, 112-124, 2015
72015
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
52020
Cost-Sensitive Boosting: A Unified Approach
N Nikolaou
University of Manchester, 2016
52016
Music emotion classification
N Nikolaou
Dissertation for the Diploma of Electronic and Computer Engineering …, 2011
52011
Margin Maximization as Lossless Maximal Compression
N Nikolaou, H Reeve, G Brown
arXiv preprint arXiv:2001.10318, 2020
22020
Fast optimization of non-convex Machine Learning objectives
N Nikolaou
M.Sc. Dissertation, 2012
22012
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
12019
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
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