6 papers
Inference for location and height of peaks of a standardized field after selection
Alden Green, Jonathan Taylor
Peak inference concerns the use of local maxima ("peaks") of a noisy random field to detect and localize regions where underlying signal is present. We propose a peak inference met…
The High-Dimensional Asymptotics of Principal Component Regression
Alden Green, Elad Romanov
We study principal components regression (PCR) in an asymptotic high-dimensional regression setting, where the number of data points is proportional to the dimension. We derive exa…
A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization
Haoxin Liu, Chenghao Liu, B. Aditya Prakash
Large language models (LLMs), with demonstrated reasoning abilities across multiple domains, are largely underexplored for time-series reasoning (TsR), which is ubiquitous in the r…
Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test
Seunghoon Paik, Michael Celentano, Alden Green +1
Integral probability metrics (IPMs) constitute a general class of nonparametric two-sample tests that are based on maximizing the mean difference between samples from one distribut…
Two-Sample Testing with a Graph-Based Total Variation Integral Probability Metric
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
We consider a novel multivariate nonparametric two-sample testing problem where, under the alternative, distributions and are separated in an integral probability metric ov…
CoCA: Cooperative Component Analysis
Daisy Yi Ding, Alden Green, Min Woo Sun +1
We propose Cooperative Component Analysis (CoCA), a new method for unsupervised multi-view analysis: it identifies the component that simultaneously captures significant within-vie…