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05/26/2023
Detecting Errors in Numerical Data via any Regression Model
Noise plagues many numerical datasets, where the recorded values in the ...
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12/04/2022
Coupled Bootstrap Test Error Estimation for Poisson Variables
Test error estimation is a fundamental problem in statistics and machine...
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11/09/2022
On High-Dimensional Gaussian Comparisons For Cross-Validation
We derive high-dimensional Gaussian comparison results for the standard ...
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05/17/2022
A Novel K-Repetition Design for SCMA
This work presents a novel K-Repetition based HARQ scheme for LDPC coded...
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03/03/2022
From local to global gene co-expression estimation using single-cell RNA-seq data
In genomics studies, the investigation of the gene relationship often br...
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11/17/2021
Unbiased Risk Estimation in the Normal Means Problem via Coupled Bootstrap Techniques
We study a new method for estimating the risk of an arbitrary estimator ...
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10/14/2021
A Distribution-Free Independence Test for High Dimension Data
Test of independence is of fundamental importance in modern data analysi...
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02/15/2021
Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests
A central goal in experimental high energy physics is to detect new phys...
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10/14/2020
A Distribution-Free Test of Covariate Shift Using Conformal Prediction
Covariate shift is a common and important assumption in transfer learnin...
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03/18/2020
Tail Bounds for Matrix Quadratic Forms and Bias Adjusted Spectral Clustering in Multi-layer Stochastic Block Models
We develop tail probability bounds for matrix linear combinations with m...
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11/19/2019
Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning
Sparse principal component analysis (PCA) is an important technique for ...
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12/21/2018
Global and Local Two-Sample Tests via Regression
Two-sample testing is a fundamental problem in statistics. Despite its l...
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04/27/2018
Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces
We provide upper bounds of the expected Wasserstein distance between a p...
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02/27/2018
Network Representation Using Graph Root Distributions
Exchangeable random graphs serve as an important probabilistic framework...
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08/01/2017
Fast Exact Conformalization of Lasso using Piecewise Linear Homotopy
Conformal prediction is a general method that converts almost any point ...
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03/23/2017
Cross-Validation with Confidence
Cross-validation is one of the most popular model selection methods in s...
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09/02/2016
Least Ambiguous Set-Valued Classifiers with Bounded Error Levels
In most classification tasks there are observations that are ambiguous a...
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05/08/2016
On-Average KL-Privacy and its equivalence to Generalization for Max-Entropy Mechanisms
We define On-Average KL-Privacy and present its properties and connectio...
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04/14/2016
Distribution-Free Predictive Inference For Regression
We develop a general framework for distribution-free predictive inferenc...
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02/13/2016
A Minimax Theory for Adaptive Data Analysis
In adaptive data analysis, the user makes a sequence of queries on the d...
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02/23/2015
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle
While machine learning has proven to be a powerful data-driven solution ...
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11/06/2014
A Generic Sample Splitting Approach for Refined Community Recovery in Stochastic Block Models
We propose and analyze a generic method for community recovery in stocha...
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08/06/2014
The functional mean-shift algorithm for mode hunting and clustering in infinite dimensions
We introduce the functional mean-shift algorithm, an iterative algorithm...
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01/27/2014
Sparsistency and agnostic inference in sparse PCA
The presence of a sparse "truth" has been a constant assumption in the t...
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12/07/2013
Consistency of spectral clustering in stochastic block models
We analyze the performance of spectral clustering for community extracti...
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02/26/2013
A Conformal Prediction Approach to Explore Functional Data
This paper applies conformal prediction techniques to compute simultaneo...
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11/02/2012
Minimax sparse principal subspace estimation in high dimensions
We study sparse principal components analysis in high dimensions, where ...
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02/03/2012
