Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

We survey recent work on neurosymbolic programming, an emerging area that bridges the
areas of deep learning and program synthesis. Like in classic machine learning, the goal …

We extend conformal prediction methodology beyond the case of exchangeable data. In
particular, we show that a weighted version of conformal prediction can be used to compute …

We propose Regularized Learning under Label shifts (RLLS), a principled and a practical
domain-adaptation algorithm to correct for shifts in the label distribution between a source …

Contraction theory is an analytical tool to study differential dynamics of a non-autonomous
(ie, time-varying) nonlinear system under a contraction metric defined with a uniformly …

As the use of machine learning in high impact domains becomes widespread, the
importance of evaluating safety has increased. An important aspect of this is evaluating how …

Conformal prediction: A unified review of theory and new challenges Page 1 Bernoulli 29(1),
2023, 1–23 https://doi.org/10.3150/21-BEJ1447 Conformal prediction: A unified review of …

Abstract We propose\textbf {JAWS}, a series of wrapper methods for distribution-free
uncertainty quantification tasks under covariate shift, centered on the core method\textbf …

We study the problem of safe learning and exploration in sequential control problems. The
goal is to safely collect data samples from operating in an environment, in order to learn to …

Learning-based control algorithms require data collection with abundant supervision for
training. Safe exploration algorithms ensure the safety of this data collection process even …