Conformal prediction beyond exchangeability
Conformal prediction beyond exchangeability Page 1 The Annals of Statistics 2023, Vol. 51, No.
2, 816–845 https://doi.org/10.1214/23-AOS2276 © Institute of Mathematical Statistics, 2023 …
2, 816–845 https://doi.org/10.1214/23-AOS2276 © Institute of Mathematical Statistics, 2023 …
A gentle introduction to conformal prediction and distribution-free uncertainty quantification
AN Angelopoulos, S Bates - arXiv preprint arXiv:2107.07511, 2021 - arxiv.org
Black-box machine learning models are now routinely used in high-risk settings, like
medical diagnostics, which demand uncertainty quantification to avoid consequential model …
medical diagnostics, which demand uncertainty quantification to avoid consequential model …
Game-theoretic statistics and safe anytime-valid inference
Safe anytime-valid inference (SAVI) provides measures of statistical evidence and certainty—
e-processes for testing and confidence sequences for estimation—that remain valid at all …
e-processes for testing and confidence sequences for estimation—that remain valid at all …
Conformal prediction interval for dynamic time-series
We develop a method to construct distribution-free prediction intervals for dynamic time-
series, called\Verb| EnbPI| that wraps around any bootstrap ensemble estimator to construct …
series, called\Verb| EnbPI| that wraps around any bootstrap ensemble estimator to construct …
[图书][B] Algorithmic learning in a random world
Vladimir Vovk Alexander Gammerman Glenn Shafer Second Edition Page 1 Vladimir Vovk
Alexander Gammerman Glenn Shafer Algorithmic Learning in a Random World Second …
Alexander Gammerman Glenn Shafer Algorithmic Learning in a Random World Second …
Testing randomness online
V Vovk - Statistical Science, 2021 - projecteuclid.org
Testing Randomness Online Page 1 Statistical Science 2021, Vol. 36, No. 4, 595–611 https://doi.org/10.1214/20-STS817
© Institute of Mathematical Statistics, 2021 Testing Randomness Online Vladimir Vovk …
© Institute of Mathematical Statistics, 2021 Testing Randomness Online Vladimir Vovk …
Conformal prediction: A gentle introduction
AN Angelopoulos, S Bates - Foundations and Trends® in …, 2023 - nowpublishers.com
Black-box machine learning models are now routinely used in high-risk settings, like
medical diagnostics, which demand uncertainty quantification to avoid consequential model …
medical diagnostics, which demand uncertainty quantification to avoid consequential model …
Boundary loss for remote sensing imagery semantic segmentation
A Bokhovkin, E Burnaev - International Symposium on Neural Networks, 2019 - Springer
In response to the growing importance of geospatial data, its analysis including semantic
segmentation becomes an increasingly popular task in computer vision today. Convolutional …
segmentation becomes an increasingly popular task in computer vision today. Convolutional …
Real-time out-of-distribution detection in learning-enabled cyber-physical systems
F Cai, X Koutsoukos - 2020 ACM/IEEE 11th International …, 2020 - ieeexplore.ieee.org
Cyber-physical systems (CPS) greatly benefit by using machine learning components that
can handle the uncertainty and variability of the real-world. Typical components such as …
can handle the uncertainty and variability of the real-world. Typical components such as …
Adversarial examples in deep learning for multivariate time series regression
GR Mode, KA Hoque - 2020 IEEE Applied Imagery Pattern …, 2020 - ieeexplore.ieee.org
Multivariate time series (MTS) regression tasks are common in many real-world data mining
applications including finance, cybersecurity, energy, healthcare, prognostics, and many …
applications including finance, cybersecurity, energy, healthcare, prognostics, and many …