Francis Bach

Quantifying uncertainty in statistics and machine learning is crucial, but challenging in high-dimensional prediction problems. Probabilistic calibration and conformal prediction have emerged as key practical theoretically well-motivated frameworks. In this talk, I will present recent advances that allow greater flexibility in their applications, in terms of anytime guarantees and applications in multivariate prediction problems beyond univariate regression and binary classification.