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3 papers
Improving Noise Robustness through Abstractions and its Impact on Machine Learning
Alfredo Ibias, Karol Capala, Varun Ravi Varma +2
Noise is a fundamental problem in learning theory with huge effects in the application of Machine Learning (ML) methods, due to real world data tendency to be noisy. Additionally,…
Preservation of Feature Stability in Machine Learning Under Data Uncertainty for Decision Support in Critical Domains
Karol Capała, Paulina Tworek, Jose Sousa
In a world where Machine Learning (ML) is increasingly deployed to support decision-making in critical domains, providing decision-makers with explainable, stable, and relevant inp…
CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures
Luca Gherardini, Varun Ravi Varma, Karol Capala +2
The availability of large data sets is providing an impetus for driving current artificial intelligent developments. There are, however, challenges for developing solutions with sm…