Uncertainty Quantification

Can we rigorously learn and quantify the uncertainty of AI models, e.g., Large Language Models (LLMs), price predictors, or drones, under distribution shift and adversarial manipulation?

Quantified uncertainty of AI models’ predictions provides a basis of the trust on predictions. To rigorously quantify uncertainty, we have mainly leveraged learning theory, calibration, and conformal prediction.

Keywords: uncertainty quantification, calibration, conformal prediction, learning theory, distribution shift

Related Work: ICLR'20, AISTATS'20, ICLR'21, ICLR'22, arXiv'22, NeurIPS'22, Security'23, NeurIPS'24