5 papers
Decentralized Conformal Novelty Detection via Quantized Model Exchange
Kyle Loh, Yu Xiang
This work studies decentralized novelty detection with global false discovery rate (FDR) control across heterogeneous composite null distributions, without sharing the raw data due…
On the Adversarial Robustness of Learning-based Conformal Novelty Detection
Daofu Zhang, Mehrdad Pournaderi, Hanne M. Clifford +2
This paper studies the adversarial robustness of conformal novelty detection. In particular, we focus on two powerful learning-based frameworks that come with finite-sample false d…
Distributed Multiple Testing with False Discovery Rate Control in the Presence of Byzantines
Daofu Zhang, Mehrdad Pournaderi, Yu Xiang +1
This work studies distributed multiple testing with false discovery rate (FDR) control in the presence of Byzantine attacks, where an adversary captures a fraction of the nodes and…
Training-Conditional Coverage Bounds under Covariate Shift
Mehrdad Pournaderi, Yu Xiang
Conformal prediction methodology has recently been extended to the covariate shift setting, where the distribution of covariates differs between training and test data. While exist…
Training-Conditional Coverage Bounds for Uniformly Stable Learning Algorithms
Mehrdad Pournaderi, Yu Xiang
The training-conditional coverage performance of the conformal prediction is known to be empirically sound. Recently, there have been efforts to support this observation with theor…