2 papers
cs.LG2024
Preservation of Feature Stability in Machine Learning Under Data Uncertainty for Decision Support in Critical Domains
Karol CapaÅa, 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…
cs.LG2024
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,…