75 citations · 84 across the 3 of their papers we have counts for
4 papers
Robust semi-parametric signal detection in particle physics with classifiers decorrelated via optimal transport
Purvasha Chakravarti, Lucas Kania, Olaf Behnke +2
Searches for signals of new physics in particle physics are usually done by training a supervised classifier to separate a signal model from the known Standard Model physics (also…
Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests
Purvasha Chakravarti, Mikael Kuusela, Jing Lei +1
A central goal in experimental high energy physics is to detect new physics signals that are not explained by known physics. In this paper, we aim to search for new signals that ap…
Gaussian Mixture Clustering Using Relative Tests of Fit
Purvasha Chakravarti, Sivaraman Balakrishnan, Larry Wasserman
We consider clustering based on significance tests for Gaussian Mixture Models (GMMs). Our starting point is the SigClust method developed by Liu et al. (2008), which introduces a…
A Generalization of Convolutional Neural Networks to Graph-Structured Data
Yotam Hechtlinger, Purvasha Chakravarti, Jining Qin
This paper introduces a generalization of Convolutional Neural Networks (CNNs) from low-dimensional grid data, such as images, to graph-structured data. We propose a novel spatial…