Our kernel uncertainty framework is in at ICML!

Our latest work introduces the first bias–variance–covariance decomposition for kernel scores, providing a unified framework for uncertainty estimation in generative models. We show how kernel-based entropy and variance capture uncertainty across image, audio, and language generation — even in closed-source models. Published at the International Conference on Machine Learning.