Nikolaos (Nikos) Nikolaou
Publications
Journal Papers
2023
Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots
Nikolaou, N., Waldmann, I.P., Tsiaras, A., Morvan, M., Edwards, B., Yip, K.H., Thompson, A., Tinetti, G., Sarkar, S., Dawson, J.M., Borisov, V., Kasneci, G., Petkovic, M., Stepisnik, T., Al-Ubaidi, T., Bailey, R.L., Granitzer, M., Julka, S., Kern, R., Ofner, P., Wagner, S., Heppe, L., Bunse, M., Morik, K., Simoes, L.F.
RAS Techniques and Instruments, Volume 2, Issue 1, Pages 695--709
[Paper]
Autoencoder-based multimodal prediction of non-small cell lung cancer survival
Ellen, J.G., Jacob, E., Nikolaou, N., Markuzon, N.
Scientific Reports Volume 13, Issue 1, Number 15761
Lessons Learned from Ariel Data Challenge 2022: Inferring Physical Properties of Exoplanets From Next-Generation Telescopes
Yip, K. H., Changeat, C., Waldmann, I., Unlu, E. B., Forestano, R. T., Roman, A., Matcheva, K., Matchev, K. T., Stefanov, S., Podsztavek, O., Morvan, M., Nikolaou, N., Al-Refaie, A., Jenner, C., Johnson, C., Tsiaras, A., Edwards, B., Alves de Oliveira, C., Cho, J., Tinetti, G.
To appear in PMLR NeurIPS 2022 Competition Track
Fast regression of the tritium breeding ratio in fusion reactors
Mánek, P., Van Goffrier, G., Gopakumar, V., Nikolaou, N., Shimwell, J., Waldmann, I.P.
Machine Learning: Science and Technology, Volume 4, Number 1
[ArXiv preprint][Paper]
2021
PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch
Morvan, M., Tsiaras A., Nikolaou, N., Waldmann, I.P.
Publications of the Astronomical Society of the Pacific, Volume 133, Number 1021
[ArXiv preprint][Paper]
Peeking inside the Black Box: Interpreting Deep-learning Models for Exoplanet Atmospheric Retrievals
Yip, K.H., Changeat, Q., Nikolaou, N., Morvan, M., Edwards, B., Waldmann, I.P., Tinetti, G.
The Astronomical Journal, Volume 162, Number 5
[ArXiv preprint][Paper]
2020
Detrending Exoplanetary Transit Light Curves with Long Short-Term Memory Networks
Morvan, M., Nikolaou, N., Tsiaras, A., Waldmann, I.P.
The Astronomical Journal, Volume 159, Number 3, 2020
[ArXiv preprint][Paper]
2016
Cost-sensitive boosting algorithms: Do we really need them?
Nikolaou, N., Edakunni, N.U., Kull, M., Flach, P.A., Brown, G.
Machine Learning, 104(2), pages 359-384, 2016
Special issue acceptance rate 10/58 (17%)
Presented in ECML/PKDD 2016.
Selected for a plenary presentation as one of 12/129 eligible papers (top 9.3%).
[Paper] [Supplementary] [Poster] [GoogleScholar] [Bibtex] [Code] [Tutorial With Code]
Nikolaou, N., Edakunni, N.U., Kull, M., Flach, P.A., Brown, G.
Machine Learning, 104(2), pages 359-384, 2016
Special issue acceptance rate 10/58 (17%)
Presented in ECML/PKDD 2016.
Selected for a plenary presentation as one of 12/129 eligible papers (top 9.3%).
[Paper] [Supplementary] [Poster] [GoogleScholar] [Bibtex] [Code] [Tutorial With Code]
Machine Learning Conference & Workshop Papers
In the field of Machine Learning & Artificial Intelligence, conferences are the main venues of research dissemination. The conference publications below have all been peer reviewed (typically 3-5 reviewers; acceptance rates 15-60%, varying per venue).
2022
ESA-Ariel Data Challenge NeurIPS 2022: Inferring Physical Properties of Exoplanets From Next-Generation Telescopes
Yip, K.H., Waldmann, I.P., Changeat, Q., Morvan, M., Al-Refaie, A.F., Edwards, B., Nikolaou, N., Tsiaras, A., Alves de Oliveira, C., Lagage, P.-O., Jenner, C., Cho, J. YK, Thiyagalingam, J., Tinetti, G.
Competition @ NeurIPS 2022
[ArXiv preprint]
Don’t Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series Transformer
Morvan, M., Nikolaou, N., Yip, K.H., Waldmann, I.
Machine Learning for Astrophysics Workshop @ ICML 2022
[ArXiv preprint] [Paper]
Don’t Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series Transformer
Morvan, M., Nikolaou, N., Yip, K H., Waldmann, I.
AI for Earth and Space Science Workshop @ ICLR 2022
[ArXiv preprint]
2021
Lossless Compression and Generalization in Overparameterized Models: The Case of Boosting
Nikolaou, N.
Neural Compression: From Information Theory to Applications -- Workshop @ ICLR 2021
[OpenReview preprint]
2020
Inferring Causal Direction from Observational Data: A Complexity Approach
Nikolaou, N., Sechidis, K.
Machine Learning for Pharma and Healthcare Applications, ECML PKDD 2020 Workshop (PharML 2020)
[Paper] [Presentation] [Video] [ArXiv preprint]
2019
Pushing the limits of exoplanet discovery via direct imaging with deep learning
Yip, K.H., Nikolaou, N., Coronica, P., Tsiaras, A., Edwards, B., Changeat, Q.
Morvan, M., Biller, B., Hinkley, S., Salmond, J., Archer, M., Sumption, P., Choquet, E.
Soummer, R., Pueyo, L., Waldmann, I.P.
ECML/PKDD 2019.
Acceptance rate 130/734 (17.7%)
[ArXiv preprint]
2017
Gradient boosting models for photovoltaic power estimation under partial shading conditions
Nikolaou, N., Batzelis, E., Brown, G.
Presented in the 5th International Workshop on Data Analytics for Renewable Energy Integration, held at ECML/PKDD 2017.
[Paper] [Presentation] [GoogleScholar] [Bibtex]
2015
Calibrating AdaBoost for asymmetric learning
Nikolaou, N., Brown, G.
Multiple Classifier Systems, pages 112-124, 2015
Presented in Intern. Workshop on Multiple Classifier Systems - MCS 2015.
[Paper] [Presentation] [GoogleScholar] [Bibtex]
2014
Information theoretic feature selection in multi-label data through composite likelihood
Sechidis, K., Nikolaou, N., Brown, G.
Structural, Syntactic, and Statistical Pattern Recognition, pages 143-152, 2014
Presented in Intern. Workshop on Structural, Syntactic, and Statistical Pattern Recognition - SSPR 2014.
[Paper] [Presentation] [GoogleScholar] [Bibtex]
Astronomy & Biomedicine Conferences
2023
Improving survival prediction using flexible late fusion machine learning framework for multi-omics data integration
Nikolaou, N., Salazar, D., RaviPrakash, H., Goncalves, M., Arango-Argoty G.A., Burlutskiy, N., Markuzon, N., Jacob, E.
To appear in American Association for Cancer Research (AACR) Annual Meeting 2023
Autoencoder-based multimodal prediction of survival for non-small cell lung cancer
Ellen, J.G., Jacob, E., Nikolaou, N., Markuzon, N.
To appear in American Association for Cancer Research (AACR) Annual Meeting 2023
2022
Ariel x NeurIPS Competition-Inferring Physical Properties of Exoplanets From Next-Generation Telescopes
Yip, K.H., Changeat, Q., Morvan, M., Nikolaou, N., Waldmann, I.
In European Planetary Science Congress (pp. EPSC2022-133)
[Paper]
2020
Mapping Mineralogical Distributions on Mars with Unsupervised Machine Learning
Hipperson, M., Waldmann, I. Grindrod, P., Nikolaou, N.
In European Planetary Science Congress Vol. 14, EPSC2020-773
[Paper]
A Deep Learning Pipeline for Unified Modelling of Time-Correlated Noise in Exoplanets Observations
Morvan, M., Nikolaou, N., Tsiaras, A., Waldmann, I.,
In European Planetary Science Congress Vol. 14, EPSC2020-373
[Paper]
Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals
Yip, K.H., Changeat, Q., Nikolaou, N., Morvan, M., Edwards, B., Waldmann, I.
In European Planetary Science Congress Vol. 14, EPSC2020-66
[Paper]
2019
Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning
Yip, K.H., Nikolaou, N., Coronica, P., Tsiaras, A., Edwards, B., Changeat, Q.
Morvan, M., Biller, B., Hinkley, S., Salmond, J., Archer, M., Sumption, P., Choquet, E.
Soummer, R., Pueyo, L., Waldmann, I.P.
EPSC-DPS Joint Meeting, EPSC-DPS2019-621
[Paper]
Correcting Transiting Exoplanet Light Curves for Stellar Spots: A Machine Learning Challenge for the ESA Ariel Space Mission
Nikolaou, N., Waldmann, I., Sarkar, S. Tsiaras, A., Edwards, B., Morvan, M., Yip, K.H., Tinetti, G.
EPSC-DPS Joint Meeting EPSC-DPS2019-817
[Paper]
Correcting Transiting Exoplanet Light Curves for Stellar Spots: A Machine Learning Challenge for the ESA Ariel Space Mission
Nikolaou, N., Waldmann, I., Sarkar, S. Tsiaras, A., Edwards, B., Morvan, M., Yip, K.H., Tinetti, G.
AAS/Division for Extreme Solar Systems Vol. 51, 330-07
[Abstract]
Preprints
2021
Ariel: Enabling planetary science across light-years
Giovanna Tinetti et al.
[ArXiv preprint]
2020
Margin Maximization as Lossless Maximal Compression
Nikolaou, N., Reeve, H., Brown G.
[ArXiv preprint]
Better Boosting with Bandits for Online Learning
Nikolaou, N, Mellor, J., Oza, N.C., Brown, G.
[ArXiv preprint]
Theses
2016
Cost-sensitive Boosting: A Unified Approach
Nikolaou, N.
Ph.D. Thesis, Supervisor: Gavin Brown
School of Computer Science, University of Manchester, 2016
[pdf] [GoogleScholar]
2012
Fast Optimization of Non-convex Machine Learning Objectives
Nikolaou, N.
M.Sc. Dissertation, Supervisor: Iain Murray
School of Informatics, University of Edinburgh, 2012
[pdf] [GoogleScholar]
2011
Music Emotion Classification
Nikolaou, N.
Undergraduate Thesis, Supervisor: Alexandros Potamianos
Department of Electronic and Computer Engineering, Technical University of Crete, 2011
[pdf] [GoogleScholar]