3 citations · 6 across the 10 of their papers we have counts for
10 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…
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…
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…
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…
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…
The Voronoigram: Minimax Estimation of Bounded Variation Functions From Scattered Data
Addison J. Hu, Alden Green, Ryan J. Tibshirani
We consider the problem of estimating a multivariate function of bounded variation (BV), from noisy observations made at random design points $x_i \in…
Minimax Optimal Regression over Sobolev Spaces via Laplacian Eigenmaps on Neighborhood Graphs
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
In this paper we study the statistical properties of Principal Components Regression with Laplacian Eigenmaps (PCR-LE), a method for nonparametric regression based on Laplacian Eig…
Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
In this paper we study the statistical properties of Laplacian smoothing, a graph-based approach to nonparametric regression. Under standard regularity conditions, we establish upp…
Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
We study the Personalized PageRank (PPR) algorithm, a local spectral method for clustering, which extracts clusters using locally-biased random walks around a given seed node. In c…