5 papers · 1 filter
Multi-source conformal prediction: leveraging heterogeneity via localization
Rohan Hore, Anirban Chatterjee, Sayantan Choudhury
Many modern prediction tasks involve data from multiple heterogeneous sources, while the test distribution may differ substantially from any individual source. Although heterogenei…
Distribution-free inference on the number of changepoints
Rohan Hore, Aaditya Ramdas
Suppose we are given an ordered sequence of independent data whose distribution changes times at unknown locations, for some unknown . In this paper, we study the pro…
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 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…
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…