3 citations · 3 across the 4 of their papers we have counts for
5 papers
Synthetic-Powered Multiple Testing with FDR Control
Yonghoon Lee, Meshi Bashari, Edgar Dobriban +1
Multiple hypothesis testing with false discovery rate (FDR) control is a fundamental problem in statistical inference, with broad applications in genomics, drug screening, and outl…
General Synthetic-Powered Inference
Meshi Bashari, Yonghoon Lee, Roy Maor Lotan +2
The rapid proliferation of high-quality synthetic data -- generated by advanced AI models or collected as auxiliary data from related tasks -- presents both opportunities and chall…
Synthetic-Powered Predictive Inference
Meshi Bashari, Roy Maor Lotan, Yonghoon Lee +2
Conformal prediction is a framework for predictive inference with a distribution-free, finite-sample guarantee. However, it tends to provide uninformative prediction sets when cali…
Robust Conformal Outlier Detection under Contaminated Reference Data
Meshi Bashari, Matteo Sesia, Yaniv Romano
Conformal prediction is a flexible framework for calibrating machine learning predictions, providing distribution-free statistical guarantees. In outlier detection, this calibratio…
Derandomized Novelty Detection with FDR Control via Conformal E-values
Meshi Bashari, Amir Epstein, Yaniv Romano +1
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…