Papers by Nikolaos (Nikos) Nikolaou
The Astronomical Journal, Feb 18, 2020
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arXiv (Cornell University), Jul 6, 2022
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arXiv (Cornell University), Jan 28, 2020
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arXiv (Cornell University), Jan 16, 2020
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<p>The study of extra-solar planets, or simply, exoplanets,  planets o... more <p>The study of extra-solar planets, or simply, exoplanets,  planets outside our own Solar System, is fundamentally a grand quest to understand our place in the Universe. Discoveries in the last two decades have re-defined what we know about planets, and helped us comprehend the uniqueness of our very own Earth. In recent years, however, the focus has shifted from planet detection to planet characterisation, where key planetary properties are inferred from telescope observations using Monte Carlo-based methods. However, the efficiency of sampling-based methodologies is put under strain by the high-resolution observational data from next generation telescopes, such as the James Webb Space Telescope and the Ariel Space Mission. We propose to host a regular competition with the goal of identifying a reliable and scalable method to perform planetary characterisation. Depending on the chosen track, participants will provide either quartile estimates or the approximate distribution  of key planetary properties. They will have access to synthetic spectroscopic data generated from the official simulators for the ESA Ariel Space Mission. The aims of the competition are three-fold. 1) To offer a challenging application for comparing and advancing conditional density estimation methods. 2) To provide a valuable contribution towards reliable and efficient analysis of spectroscopic data, enabling astronomers to build a better picture of planetary demographics, and 3) To promote the interaction between ML and exoplanetary science.</p> <p>The competition is open for all and is expected to run from July to October. We will provide a brief introduction to the competition, its aim and the different tracks available for participants. We will also be sharing preliminary results from the competition in this session.</p> <p> </p>
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AAS/Division for Extreme Solar Systems Abstracts, Aug 1, 2019
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The Astronomical Journal, Oct 13, 2021
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Lecture Notes in Computer Science, 2017
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Machine Learning, 2016
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The Astronomical Journal, 2020
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Lecture Notes in Computer Science, 2014
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Machine Learning and Knowledge Discovery in Databases, 2020
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Abstract. Asymmetric classification problems are characterized by class imbalance or unequal cost... more Abstract. Asymmetric classification problems are characterized by class imbalance or unequal costs for different types of misclassifications. One of the main cited weaknesses of AdaBoost is its perceived inability to handle asymmetric problems. As a result, a multitude of asymmetric versions of AdaBoost have been proposed, mainly as heuristic modifications to the original algorithm. In this paper we challenge this approach and propose instead handling asymmetric tasks by properly calibrating the scores of the original AdaBoost so that they correspond to probability estimates. We then account for the asymmetry using classic decision theoretic ap-proaches. Empirical comparisons of this approach against the most repre-sentative asymmetric Adaboost variants show that it compares favorably. Moreover, it retains the theoretical guarantees of the original AdaBoost and it can easily be adjusted to account for changes in class imbalance or costs without need for retraining.
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ArXiv, 2020
Probability estimates generated by boosting ensembles are poorly calibrated because of the margin... more Probability estimates generated by boosting ensembles are poorly calibrated because of the margin maximization nature of the algorithm. The outputs of the ensemble need to be properly calibrated before they can be used as probability estimates. In this work, we demonstrate that online boosting is also prone to producing distorted probability estimates. In batch learning, calibration is achieved by reserving part of the training data for training the calibrator function. In the online setting, a decision needs to be made on each round: shall the new example(s) be used to update the parameters of the ensemble or those of the calibrator. We proceed to resolve this decision with the aid of bandit optimization algorithms. We demonstrate superior performance to uncalibrated and naively-calibrated on-line boosting ensembles in terms of probability estimation. Our proposed mechanism can be easily adapted to other tasks(e.g. cost-sensitive classification) and is robust to the choice of hyper...
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ArXiv, 2021
The tritium breeding ratio (TBR) is an essential quantity for the design of modern and next-gener... more The tritium breeding ratio (TBR) is an essential quantity for the design of modern and next-generation D-T fueled nuclear fusion reactors. Representing the ratio between tritium fuel generated in breeding blankets and fuel consumed during reactor runtime, the TBR depends on reactor geometry and material properties in a complex manner. In this work, we explored the training of surrogate models to produce a cheap but high-quality approximation for a Monte Carlo TBR model in use at the UK Atomic Energy Authority. We investigated possibilities for dimensional reduction of its feature space, reviewed 9 families of surrogate models for potential applicability, and performed hyperparameter optimisation. Here we present the performance and scaling properties of these models, the fastest of which, an artificial neural network, demonstrated R =0.985 and a mean prediction time of 0.898μs, representing a relative speedup of 8 · 10 with respect to the expensive MC model. We further present a nov...
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ArXiv, 2020
The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisa... more The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots. The current practice in the literature is to identify the effects of spots visually and correct for them manually or discard the affected data. This paper explores a first step towards fully automating the efficient and precise derivation of transit depths from transit light curves in the presence of stellar spots. The methods and results we present were obtained in the context of the 1st Machine Learning Challenge organized for the European Space Agency's upcoming Ariel mission. We first present the problem, the simulated Ariel-like data and outline the Challenge while identifying best pra...
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Papers by Nikolaos (Nikos) Nikolaou
our own dataset of annotated song samples and we examined two distinct methods of describing an emotion: using clusters consisting of various emotional states, and using a two-dimensional representation of the emotion in the Valence-Activation plane. The latter method was chosen as the most successful. We also tried other approaches of music emotion classification (MEC) as well, such as treating the song sample as an amplitude and frequency modulated (AM-FM) signal, on which we subsequently perform multiband demodulation analysis (MDA) testing various Gabor filter banks (Mel scale-based filter bank, Bark scale-based filter bank, and a number of fractional octave-based filter banks). Statistics of the Frequency Modulation Percentages (FMPs) of each band derived from the demodulation, proved to be quite successful features in the classification of emotion. Finally, we
explored other modalities besides the music sound signal itself, such as a number of features derived from the chords of the song samples, classification of the song samples' lyrics using various techniques and a brief investigation of Electroencephalogram (EEG) data generated by one of the annotators
while performing the annotation of the song samples. Our final feature-pack included a combination of the most successful features among the ones we studied: (i) music-inspired features (features based on music theory and psychoacoustics, derived from either the sound signal or the chords of the sample), (ii) statistics of the FMPs and (iii) statistics of the Mel-frequency cepstral coefficients (MFCCs). This feature-pack proved to be more robust than its three individual components and in the end we achieved results that reached 85.7% correct classification rate in the dimension of Valence and 85.1% correct classification rate in the dimension of Activation. We finally demonstrate that by discarding training samples that are assigned a label too close to the neutral value, our results can improve even further, especially in the dimension of Activation.