289 citations · 1.2k across the 72 of their papers we have counts for
11 papers · 1 filter
Distribution-free inference with hierarchical data
Yonghoon Lee, Rina Foygel Barber, Rebecca Willett
This paper studies distribution-free inference in settings where the data set has a hierarchical structure -- for example, groups of observations, or repeated measurements. In such…
Functional Linear Regression with Mixed Predictors
Daren Wang, Zifeng Zhao, Yi Yu +1
We study a functional linear regression model that deals with functional responses and allows for both functional covariates and high-dimensional vector covariates. The proposed mo…
An Optimal Statistical and Computational Framework for Generalized Tensor Estimation
Rungang Han, Rebecca Willett, Anru R. Zhang
This paper describes a flexible framework for generalized low-rank tensor estimation problems that includes many important instances arising from applications in computational imag…
Localizing Changes in High-Dimensional Vector Autoregressive Processes
Daren Wang, Yi Yu, Alessandro Rinaldo +1
Autoregressive models capture stochastic processes in which past realizations determine the generative distribution of new data; they arise naturally in a variety of industrial, bi…
Statistically and Computationally Efficient Change Point Localization in Regression Settings
Daren Wang, Zifeng Zhao, Kevin Lin +1
Detecting when the underlying distribution changes for the observed time series is a fundamental problem arising in a broad spectrum of applications. In this paper, we study multip…
A data-dependent weighted LASSO under Poisson noise
Xin Jiang, Patricia Reynaud-Bouret, Vincent Rivoirard +2
Sparse linear inverse problems appear in a variety of settings, but often the noise contaminating observations cannot accurately be described as bounded by or arising from a Gaussi…
Minimax Optimal Rates for Poisson Inverse Problems with Physical Constraints
Xin Jiang, Garvesh Raskutti, Rebecca Willett
This paper considers fundamental limits for solving sparse inverse problems in the presence of Poisson noise with physical constraints. Such problems arise in a variety of applicat…
Oracle inequalities and minimax rates for non-local means and related adaptive kernel-based methods
Ery Arias-Castro, Joseph Salmon, Rebecca Willett
This paper describes a novel theoretical characterization of the performance of non-local means (NLM) for noise removal. NLM has proven effective in a variety of empirical studies,…
A recursive procedure for density estimation on the binary hypercube
Maxim Raginsky, Jorge Silva, Svetlana Lazebnik +1
This paper describes a recursive estimation procedure for multivariate binary densities (probability distributions of vectors of Bernoulli random variables) using orthogonal expans…
Target Detection Performance Bounds in Compressive Imaging
Kalyani Krishnamurthy, Rebecca Willett, Maxim Raginsky
This paper describes computationally efficient approaches and associated theoretical performance guarantees for the detection of known targets and anomalies from few projection mea…
The False Discovery Rate for Statistical Pattern Recognition
Clayton Scott, Gowtham Bellala, Rebecca Willett
The false discovery rate (FDR) and false nondiscovery rate (FNDR) have received considerable attention in the literature on multiple testing. These performance measures are also ap…