1 citations · 2 across the 10 of their papers we have counts for
14 papers
Sketched Calibration for Conformal Prediction under Covariate Shift
Mehrdad Pournaderi
Weighted conformal prediction corrects for covariate shift by reweighting calibration scores with the likelihood ratio between target and source covariates. Its cost grows with the…
Calibrated Order-Randomized Rosenblatt Tests
Mehrdad Pournaderi
We test whether a multivariate vector X conforms to a specified distribution F, a problem in copula modelling and density forecasting. The Rosenblatt transform reduces it to a test…
Sharp training-conditional coverage for conformal prediction under covariate shift
Mehrdad Pournaderi
Weighted split conformal prediction reweights calibration scores by the likelihood ratio between the test and training covariate distributions and guarantees marginal coverage unde…
Finite-Sample FDR Control for Greedy Aggregation over Networks
Mehrdad Pournaderi
Distributed multiple testing asks sites, each holding p-values for its own hypotheses, to control the false discovery rate (FDR) of the discoveries made across the whole networ…
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…
Communication-Efficient Distribution-Free Inference Over Networks
Mehrdad Pournaderi, Yu Xiang
Consider a star network where each local node possesses a set of test statistics that exhibit a symmetric distribution around zero when their corresponding null hypothesis is true.…
On Large-Scale Multiple Testing Over Networks: An Asymptotic Approach
Mehrdad Pournaderi, Yu Xiang
This work concerns developing communication- and computation-efficient methods for large-scale multiple testing over networks, which is of interest to many practical applications.…
Sample-and-Forward: Communication-Efficient Control of the False Discovery Rate in Networks
Mehrdad Pournaderi, Yu Xiang
This work concerns controlling the false discovery rate (FDR) in networks under communication constraints. We present sample-and-forward, a flexible and communication-efficient ver…
Variable Selection with the Knockoffs: Composite Null Hypotheses
Mehrdad Pournaderi, Yu Xiang
The fixed-X knockoff filter is a flexible framework for variable selection with false discovery rate (FDR) control in linear models with arbitrary design matrices (of full column r…
Communication-Efficient Distributed Multiple Testing for Large-Scale Inference
Mehrdad Pournaderi, Yu Xiang
The Benjamini-Hochberg (BH) procedure is a celebrated method for multiple testing with false discovery rate (FDR) control. In this paper, we consider large-scale distributed networ…
Differentially Private Variable Selection via the Knockoff Filter
Mehrdad Pournaderi, Yu Xiang
The knockoff filter, recently developed by Barber and Candes, is an effective procedure to perform variable selection with a controlled false discovery rate (FDR). We propose a pri…