3 citations · 4 across the 6 of their papers we have counts for
8 papers
aRieL - Reinforcement Learning for the Ariel Space Telescope Scheduling
James Kostas Ray, Kai Hou Yip, Alexandra Thompson +2
The Ariel mission will conduct a population level survey of hundreds of exoplanet atmospheres, requiring observations to be scheduled across a large and diverse catalogue of potent…
Graph neural networks for exoplanet atmospheres
Antonia Vojtekova, Kai Hou Yip, Ingo P. Waldmann +4
Calculating disequilibrium chemistry in exoplanet atmospheres remains a significant computational bottleneck in atmospheric retrievals. The increasing observational precision from…
Gaussian perturbation interpretability for regression techniques to understand retrieval black-boxes
Jools D. Clarke, Kai Hou Yip, Gordon Yip +1
Characterizing the atmospheres of large numbers of exoplanets from their observed spectra can answer key questions about planetary populations. Traditional retrievals are resource-…
Adaptive Online Emulation for Accelerating Complex Physical Simulations
Tara P. A. Tahseen, Nikolaos Nikolaou, Luís F. Simões +3
Complex physical simulations often require trade-offs between model fidelity and computational feasibility. We introduce Adaptive Online Emulation (AOE), which dynamically learns n…
Extreme Learning Machines for Exoplanet Simulations: A Faster, Lightweight Alternative to Deep Learning
Tara P. A. Tahseen, Luís F. Simões, Kai Hou Yip +3
Increasing resolution and coverage of astrophysical and climate data necessitates increasingly sophisticated models, often pushing the limits of computational feasibility. While em…
Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling
David Svensson, Erik Hermansson, Nikolaos Nikolaou +2
In recent years, two parallel research trends have emerged in machine learning, yet their intersections remain largely unexplored. On one hand, there has been a significant increas…
Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach
R. P. Nathan, Nikolaos Nikolaou, Ofer Lahav
Unsupervised machine learning methods are well suited to searching for anomalies at scale but can struggle with the high-dimensional representation of many modern datasets, hence d…
Planetary Edge Trends (PET). I. The Inner Edge-Stellar Mass Correlation
Meng-Fei Sun, Ji-Wei Xie, Ji-Lin Zhou +3
The position of the innermost planet (i.e., the inner edge) in a planetary system provides important information about the relationship of the entire system to its host star proper…