8 papers
Local permutation tests for conditional independence: an adaptive binning perspective
David Chen, Rohan Hore, Rina Foygel Barber
In this work, we study the problem of testing conditional independence between random variables and given a confounder . The local permutation test (LPT) offers a princi…
One-shot Conditional Sampling: MMD meets Nearest Neighbors
Anirban Chatterjee, Sayantan Choudhury, Rohan Hore
How can we generate samples from a conditional distribution that we never fully observe? This question arises across a broad range of applications in both modern machine learning a…
Online monotone density estimation and log-optimal calibration
Rohan Hore, Ruodu Wang, Aaditya Ramdas
We study the problem of online monotone density estimation, where density estimators must be constructed in a predictable manner from sequentially observed data. We propose two onl…
Distribution-free root cause analysis
Rohan Hore, Aaditya Ramdas
We study distribution-free root cause analysis in multi-stream data, where an evolving underlying system is observed through multiple data streams that may each undergo distributio…
Testing conditional independence under isotonicity
Rohan Hore, Jake A. Soloff, Rina Foygel Barber +1
We propose a test of the conditional independence of random variables and~ given~ under the additional assumption that is stochastically nondecreasing in~. The wel…
Distribution-free two-sample testing with blurred total variation distance
Rohan Hore, Rina Foygel Barber
Two-sample testing, where we aim to determine whether two distributions are equal or not equal based on samples from each one, is challenging if we cannot place assumptions on the…