State of the Art Calibration

State-of-the-art calibration research focuses on improving the reliability and trustworthiness of predictive models by ensuring their output probabilities accurately reflect true likelihoods. Current efforts address challenges like out-of-distribution generalization, online prediction scenarios, and the calibration of complex models such as vision-language models used in zero-shot inference, employing techniques ranging from maximum entropy loss functions to novel approaches based on approachability theory and differentiable rendering. These advancements are crucial for enhancing the safety and dependability of machine learning systems across diverse applications, from robotics (e.g., hand-eye calibration) to computer vision.