12 papers
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…
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…
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…
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…
Conformal changepoint localization
Rohan Hore, Aaditya Ramdas
We study the problem of offline changepoint localization in a distribution-free setting. One observes a vector of data with a single changepoint, assuming that the data before and…
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…
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…
Conformal prediction with local weights: randomization enables local guarantees
Rohan Hore, Rina Foygel Barber
In this work, we consider the problem of building distribution-free prediction intervals with finite-sample conditional coverage guarantees. Conformal prediction (CP) is an increas…
Conformalized survival analysis with adaptive cutoffs
Yu Gui, Rohan Hore, Zhimei Ren +1
This paper introduces an assumption-lean method that constructs valid and efficient lower predictive bounds (LPBs) for survival times with censored data. We build on recent work by…
Robust and Efficient Parameter Estimation for Discretely Observed Stochastic Processes
Rohan Hore, Abhik Ghosh
In various practical situations, we encounter data from stochastic processes which can be efficiently modelled by an appropriate parametric model for subsequent statistical analyse…