Derandomized Novelty Detection with FDR Control via Conformal E-values. (arXiv:2302.07294v1 [cs.LG])

Conformal prediction and other randomized model-free inference techniques are
gaining increasing attention as general solutions to rigorously calibrate the
output of any machine learning algorithm for novelty detection. This paper
contributes to the field by developing a novel method for mitigating their
algorithmic randomness, leading to an even more interpretable and reliable
framework for powerful novelty detection under false discovery rate control.
The idea is to leverage suitable conformal e-values instead of p-values to
quantify the significance of each finding, which allows the evidence gathered
from multiple mutually dependent analyses of the same data to be seamlessly
aggregated. Further, the proposed method can reduce randomness without much
loss of power, partly thanks to an innovative way of weighting conformal
e-values based on additional side information carefully extracted from the same
data. Simulations with synthetic and real data confirm this solution can be
effective at eliminating random noise in the inferences obtained with
state-of-the-art alternative techniques, sometimes also leading to higher
power.