| Cost-sensitive boosting algorithms: Do we really need them? N Nikolaou, N Edakunni, M Kull, P Flach, G Brown Machine Learning 104, 359-384, 2016 | 80 | 2016 |
| Ariel: Enabling planetary science across light-years G Tinetti, P Eccleston, C Haswell, PO Lagage, J Leconte, T Lüftinger, ... arXiv preprint arXiv:2104.04824, 2021 | 47 | 2021 |
| Information theoretic feature selection in multi-label data through composite likelihood K Sechidis, N Nikolaou, G Brown Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR
, 2014 | 25 | 2014 |
| 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, ... The Astronomical Journal 162 (5), 195, 2021 | 20 | 2021 |
| 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 | 18 | 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, ... RAS Techniques and Instruments 2 (1), 695-709, 2023 | 14 | 2023 |
| 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 | 13 | 2021 |
| Calibrating AdaBoost for asymmetric learning N Nikolaou, G Brown Multiple Classifier Systems: 12th International Workshop, MCS 2015, Günzburg
, 2015 | 11 | 2015 |
| Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series Transformer M Morvan, N Nikolaou, KH Yip, I Waldmann arXiv preprint arXiv:2207.02777, 2022 | 10 | 2022 |
| Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning KH Yip, N Nikolaou, P Coronica, A Tsiaras, B Edwards, Q Changeat, ... In Proc. of Machine Learning and Knowledge Discovery in Databases, ECML PKDD
, 2019 | 9 | 2019 |
| Autoencoder-based multimodal prediction of non-small cell lung cancer survival JG Ellen, E Jacob, N Nikolaou, N Markuzon Scientific Reports 13 (1), 15761, 2023 | 7 | 2023 |
| Cost-Sensitive Boosting: A Unified Approach N Nikolaou University of Manchester, 2016 | 6 | 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 | 4 | 2020 |
| Fast optimization of non-convex Machine Learning objectives N Nikolaou M.Sc. Dissertation, 2012 | 3 | 2012 |
| ESA-Ariel Data Challenge NeurIPS 2022: Inferring Physical Properties of Exoplanets From Next-Generation Telescopes KH Yip, IP Waldmann, Q Changeat, M Morvan, AF Al-Refaie, B Edwards, ... arXiv preprint arXiv:2206.14642, 2022 | 2 | 2022 |
| Quantifying the advantage of multimodal data fusion for survival prediction in cancer patients N Nikolaou, D Salazar, H RaviPrakash, M Goncalves, R Mulla, ... bioRxiv, 2024.01. 08.574756, 2024 | 1 | 2024 |
| Lessons Learned from Ariel Data Challenge 2022-Inferring Physical Properties of Exoplanets From Next-Generation Telescopes KH Yip, Q Changeat, I Waldmann, EB Unlu, RT Forestano, A Roman, ... NeurIPS 2022 Competition Track, 1-17, 2023 | 1 | 2023 |
| Fast regression of the tritium breeding ratio in fusion reactors P Mánek, G Van Goffrier, V Gopakumar, N Nikolaou, J Shimwell, ... Machine Learning: Science and Technology 4 (1), 015008, 2023 | 1 | 2023 |
| Mapping mineralogical distributions on Mars with unsupervised machine learning M Hipperson, I Waldmann, P Grindrod, N Nikolaou EPSC2020, 2020 | 1 | 2020 |
Ingo WaldmannUniversity College Londonยืนยันอีเมลแล้วที่ star.ucl.ac.uk
