Adaptive novelty detection with false discovery rate guarantee
Adaptive novelty detection with false discovery rate guarantee Page 1 The Annals of Statistics
2024, Vol. 52, No. 1, 157–183 https://doi.org/10.1214/23-AOS2338 © Institute of Mathematical …
2024, Vol. 52, No. 1, 157–183 https://doi.org/10.1214/23-AOS2338 © Institute of Mathematical …
Derandomized novelty detection with FDR control via conformal e-values
Conformal inference provides a general distribution-free method to rigorously calibrate the
output of any machine learning algorithm for novelty detection. While this approach has …
output of any machine learning algorithm for novelty detection. While this approach has …
Conformal inference is (almost) free for neural networks trained with early stopping
Early stopping based on hold-out data is a popular regularization technique designed to
mitigate overfitting and increase the predictive accuracy of neural networks. Models trained …
mitigate overfitting and increase the predictive accuracy of neural networks. Models trained …
Confidence on the focal: Conformal prediction with selection-conditional coverage
Conformal prediction builds marginally valid prediction intervals which cover the unknown
outcome of a randomly drawn new test point with a prescribed probability. In practice, a …
outcome of a randomly drawn new test point with a prescribed probability. In practice, a …
Model-free selective inference under covariate shift via weighted conformal p-values
This paper introduces weighted conformal p-values for model-free selective inference.
Assume we observe units with covariates $ X $ and missing responses $ Y $, the goal is to …
Assume we observe units with covariates $ X $ and missing responses $ Y $, the goal is to …
Conformal frequency estimation using discrete sketched data with coverage for distinct queries
This paper develops conformal inference methods to construct a confidence interval for the
frequency of a queried object in a very large discrete data set, based on a sketch with a …
frequency of a queried object in a very large discrete data set, based on a sketch with a …
Adaptive conformal classification with noisy labels
This paper develops novel conformal prediction methods for classification tasks that can
automatically adapt to random label contamination in the calibration sample, enabling more …
automatically adapt to random label contamination in the calibration sample, enabling more …
A burden shared is a burden halved: A fairness-adjusted approach to classification
We investigate fairness in classification, where automated decisions are made for
individuals from different protected groups. In high-consequence scenarios, decision errors …
individuals from different protected groups. In high-consequence scenarios, decision errors …
Conformal link prediction to control the error rate
A Marandon - arXiv preprint arXiv:2306.14693, 2023 - arxiv.org
Most link prediction methods return estimates of the connection probability of missing edges
in a graph. Such output can be used to rank the missing edges, from most to least likely to be …
in a graph. Such output can be used to rank the missing edges, from most to least likely to be …
Simultaneous hypothesis testing using internal negative controls with an application to proteomics
Z Gao, Q Zhao - arXiv preprint arXiv:2303.01552, 2023 - arxiv.org
Negative control is a common technique in scientific investigations and broadly refers to the
situation where a null effect (''negative result'') is expected. Motivated by a real proteomic …
situation where a null effect (''negative result'') is expected. Motivated by a real proteomic …