Set Valued

Set-valued methods in machine learning address the challenge of representing and predicting multiple possible outcomes, rather than a single definitive prediction. Current research focuses on developing robust and efficient algorithms for set classification, often employing prototype-based models, neural networks trained with set-based approaches, and conformal prediction methods to provide uncertainty quantification. These advancements are improving the robustness and reliability of machine learning models in various applications, including safety-critical systems, biological sequence analysis, and climate modeling, by explicitly accounting for uncertainty and noise in data. The ability to handle set-valued data is crucial for building more reliable and trustworthy AI systems.