We propose tests for general functionals of conditional distributions (including the two-sample test) with finite-sample guarantees and dependent data thanks to generalizations of time-uniform uncertainty bounds for kernel ridge regression.

we theoretically study when and how unlabeled data can help in multi-objective learning

Improved sample complexity bounds for agnostic binary classification in the tau-based model!