Set Valued Prediction

Set-valued prediction addresses the challenge of providing uncertainty estimates alongside point predictions in machine learning, offering prediction sets guaranteed to contain the true value with a specified probability. Current research focuses on improving the efficiency and interpretability of these sets, exploring techniques like cardinality-aware loss functions, semantic concept guidance, and PAC-Bayes generalization bounds to optimize set size and coverage. These advancements are impacting diverse fields, from object detection and video captioning, where robust uncertainty quantification is crucial, to healthcare diagnostics, where controlling both the value and cost of predictions is paramount.