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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…
Learning Parameter Distributions to Detect Concept Drift in Data Streams
Johannes Haug, Gjergji Kasneci
Data distributions in streaming environments are usually not stationary. In order to maintain a high predictive quality at all times, online learning models need to adapt to distri…
Aggregating Dependent Gaussian Experts in Local Approximation
Hamed Jalali, Gjergji Kasneci
Distributed Gaussian processes (DGPs) are prominent local approximation methods to scale Gaussian processes (GPs) to large datasets. Instead of a global estimation, they train loca…
On Counterfactual Explanations under Predictive Multiplicity
Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci
Counterfactual explanations are usually obtained by identifying the smallest change made to an input to change a prediction made by a fixed model (hereafter called sparse methods).…
Leveraging Model Inherent Variable Importance for Stable Online Feature Selection
Johannes Haug, Martin Pawelczyk, Klaus Broelemann +1
Feature selection can be a crucial factor in obtaining robust and accurate predictions. Online feature selection models, however, operate under considerable restrictions; they need…
Bias in Data-driven AI Systems -- An Introductory Survey
Eirini Ntoutsi, Pavlos Fafalios, Ujwal Gadiraju +20
AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and any…