Accuracy
Bayesian Optimization of Function Networks: Supplementary Material
We consider "make-to-order' in which custom
Spot the Difference: Detection of Topological Changes via Geometric Alignment
A recurring challenge is the alignment of domains whose topology is not the same; a problem that is routinely ignored, potentially introducing bias in downstream analysis.
Learning from Positive and Unlabeled Data with Arbitrary Positive Shift Supplemental Materials A Nomenclature
Boldface denotes the best performing algorithm according to mean misclassification rate.
Appendix CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances A Experimental details Training details
The learning rate starts at 0.1 and is dropped by a factor of 10 The detailed description of the augmentations are as follows: Inception crop. After the crop, cropped image are resized to the original image size. We apply color jitter with 80% of probability. Randomly apply a grayscale with 20% of probability. For unlabeled and labeled multi-class datasets, we train ResNet with CIFAR-10 and ImageNet-30.
Online selective conformal inference: adaptive scores, convergence rate and optimality
Humbert, Pierre, Gazin, Ulysse, Heller, Ruth, Roquain, Etienne
In a supervised online setting, quantifying uncertainty has been proposed in the seminal work of \cite{gibbs2021adaptive}. For any given point-prediction algorithm, their method (ACI) produces a conformal prediction set with an average missed coverage getting close to a pre-specified level $α$ for a long time horizon. We introduce an extended version of this algorithm, called OnlineSCI, allowing the user to additionally select times where such an inference should be made. OnlineSCI encompasses several prominent online selective tasks, such as building prediction intervals for extreme outcomes, classification with abstention, and online testing. While OnlineSCI controls the average missed coverage on the selected in an adversarial setting, our theoretical results also show that it controls the instantaneous error rate (IER) at the selected times, up to a non-asymptotical remainder term. Importantly, our theory covers the case where OnlineSCI updates the point-prediction algorithm at each time step, a property which we refer to as {\it adaptive} capability. We show that the adaptive versions of OnlineSCI can convergence to an optimal solution and provide an explicit convergence rate in each of the aforementioned application cases, under specific mild conditions. Finally, the favorable behavior of OnlineSCI in practice is illustrated by numerical experiments.