A review of domain adaptation without target labels
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 …
related fields. This review asks the question: How can a classifier learn from a source …
Neurosymbolic programming
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 …
areas of deep learning and program synthesis. Like in classic machine learning, the goal …
Conformal prediction under covariate shift
RJ Tibshirani, R Foygel Barber… - Advances in neural …, 2019 - proceedings.neurips.cc
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 …
particular, we show that a weighted version of conformal prediction can be used to compute …
Regularized learning for domain adaptation under label shifts
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 …
domain-adaptation algorithm to correct for shifts in the label distribution between a source …
Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview
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 …
(ie, time-varying) nonlinear system under a contraction metric defined with a uniformly …
Evaluating model robustness and stability to dataset shift
A Subbaswamy, R Adams… - … conference on artificial …, 2021 - proceedings.mlr.press
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 …
importance of evaluating safety has increased. An important aspect of this is evaluating how …
Conformal prediction: a unified review of theory and new challenges
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 …
2023, 1–23 https://doi.org/10.3150/21-BEJ1447 Conformal prediction: A unified review of …
Jaws: Auditing predictive uncertainty under covariate shift
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 …
uncertainty quantification tasks under covariate shift, centered on the core method\textbf …
Robust regression for safe exploration in control
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 …
goal is to safely collect data samples from operating in an environment, in order to learn to …
Chance-constrained trajectory optimization for safe exploration and learning of nonlinear systems
Learning-based control algorithms require data collection with abundant supervision for
training. Safe exploration algorithms ensure the safety of this data collection process even …
training. Safe exploration algorithms ensure the safety of this data collection process even …