Differentiable Kernel Methods with Error Certificates for Deep Learning Pipelines

Details.

This online talk presents kernel components that integrate into standard deep learning pipelines while providing a computable, geometry-aware error estimate for each prediction. It explores applications across vision, reinforcement learning, tabular data, finance and insurance, and discusses how error-aware models can make predictions more auditable.