Noisy Evaluation

Noisy evaluation, the challenge of obtaining imperfect or unreliable measurements in various machine learning tasks, is a growing area of research focusing on improving the robustness and accuracy of algorithms in the face of uncertainty. Current efforts concentrate on developing methods to mitigate the effects of noise in diverse settings, including hyperparameter tuning, evolutionary algorithms, and expert ranking, often employing techniques like Markov Decision Processes or leveraging multiple evaluations to reduce uncertainty. Addressing noisy evaluation is crucial for advancing the reliability and trustworthiness of machine learning systems across numerous applications, from medical image analysis to algorithmic recourse and federated learning.