18 citations · 37 across the 5 of their papers we have counts for
8 papers
PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch
Mario Morvan, Angelos Tsiaras, Nikolaos Nikolaou +1
We present a new open source python package, based on PyLightcurve and PyTorch, tailored for efficient computation and automatic differentiation of exoplanetary transits. The class…
Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals
Kai Hou Yip, Quentin Changeat, Nikolaos Nikolaou +4
Deep learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly non-linear relations and solve interesting problems in…
Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots
Nikolaos Nikolaou, Ingo P. Waldmann, Angelos Tsiaras +20
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 usin…
Inferring Causal Direction from Observational Data: A Complexity Approach
Nikolaos Nikolaou, Konstantinos Sechidis
At the heart of causal structure learning from observational data lies a deceivingly simple question: given two statistically dependent random variables, which one has a causal eff…
Margin Maximization as Lossless Maximal Compression
Nikolaos Nikolaou, Henry Reeve, Gavin Brown
The ultimate goal of a supervised learning algorithm is to produce models constructed on the training data that can generalize well to new examples. In classification, functional m…
Better Boosting with Bandits for Online Learning
Nikolaos Nikolaou, Joseph Mellor, Nikunj C. Oza +1
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 prope…