41 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of PennsylvaniaUS17 papers
- Wharton County Junior CollegeUS14 papers
- Rutgers, The State University of New JerseyUS3 papers
- Microsoft Research New England (United States)US2 papers
- University of the ArtsUS2 papers
- Applied Mathematics (United States)US1 paper
- Brigham and Women's HospitalUS1 paper
- Carnegie Mellon UniversityUS1 paper
- Department of Physics, Mathematics and InformaticsBY1 paper
- Harvard UniversityUS1 paper
- Kaiser Permanente Washington Health Research InstituteUS1 paper
- Massachusetts Institute of TechnologyUS1 paper
18 papers
Tackling Copyright Issues in AI Image Generation Through Originality Estimation and Genericization
Hiroaki Chiba-Okabe, Weijie J. Su
The rapid progress of generative AI technology has sparked significant copyright concerns, leading to numerous lawsuits filed against AI developers. Notably, generative AI's capaci…
A Test for Differential Ascertainment in Case-Control Studies with Application to Child Maltreatment
Matteo Sordello, Dylan S. Small
We propose a method to test for the presence of differential ascertainment in case-control studies, when data are collected by multiple sources. We show that, when differential asc…
Distributed linear regression by averaging
Edgar Dobriban, Yue Sheng
Distributed statistical learning problems arise commonly when dealing with large datasets. In this setup, datasets are partitioned over machines, which compute locally, and communi…
Capacity lower bound for the Ising perceptron
Jian Ding, Nike Sun
We consider the Ising perceptron with gaussian disorder, which is equivalent to the discrete cube intersected by random half-spaces. The perceptron's capacity is…
A general approach to detect gene (G)-environment (E) additive interaction leveraging G-E independence in case-control studies
Eric J. Tchetgen Tchetgen, Xu Shi, Tamar Sofer +1
It is increasingly of interest in statistical genetics to test for the presence of a mechanistic interaction between genetic (G) and environmental (E) risk factors by testing for t…
Multiply Robust Causal Inference with Double Negative Control Adjustment for Categorical Unmeasured Confounding
Xu Shi, Wang Miao, Jennifer C. Nelson +1
Unmeasured confounding is a threat to causal inference in observational studies. In recent years, use of negative controls to mitigate unmeasured confounding has gained increasing…
Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information
T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
We study the misclassification error for community detection in general heterogeneous stochastic block models (SBM) with noisy or partial label information. We establish a connecti…
Two-sample instrumental variable analyses using heterogeneous samples
Qingyuan Zhao, Jingshu Wang, Jack Bowden +1
Instrumental variable analysis is a widely used method to estimate causal effects in the presence of unmeasured confounding. When the instruments, exposure and outcome are not meas…
Models as Approximations II: A Model-Free Theory of Parametric Regression
Andreas Buja, Lawrence Brown, Arun Kumar Kuchibhotla +3
We develop a model-free theory of general types of parametric regression for iid observations. The theory replaces the parameters of parametric models with statistical functionals,…
Fair Algorithms for Infinite and Contextual Bandits
Matthew Joseph, Michael Kearns, Jamie Morgenstern +2
We study fairness in linear bandit problems. Starting from the notion of meritocratic fairness introduced in Joseph et al. [2016], we carry out a more refined analysis of a more ge…
A Sparse PCA Approach to Clustering
T. Tony Cai, Linjun Zhang
We discuss a clustering method for Gaussian mixture model based on the sparse principal component analysis (SPCA) method and compare it with the IF-PCA method. We also discuss the…
Covariate Balancing Propensity Score by Tailored Loss Functions
Qingyuan Zhao
In observational studies, propensity scores are commonly estimated by maxi- mum likelihood but may fail to balance high-dimensional pre-treatment covariates even after specificatio…
High-Dimensional Gaussian Copula Regression: Adaptive Estimation and Statistical Inference
T. Tony Cai, Linjun Zhang
We develop adaptive estimation and inference methods for high-dimensional Gaussian copula regression that achieve the same performance without the knowledge of the marginal transfo…
Instrumental Variables Estimation with Some Invalid Instruments and its Application to Mendelian Randomization
Hyunseung Kang, Anru Zhang, T. Tony Cai +1
Instrumental variables have been widely used for estimating the causal effect between exposure and outcome. Conventional estimation methods require complete knowledge about all the…
Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-rank Matrices
T. Tony Cai, Anru Zhang
This paper considers compressed sensing and affine rank minimization in both noiseless and noisy cases and establishes sharp restricted isometry conditions for sparse signal and lo…
A tail inequality for quadratic forms of subgaussian random vectors
Daniel Hsu, Sham M. Kakade, Tong Zhang
We prove an exponential probability tail inequality for positive semidefinite quadratic forms in a subgaussian random vector. The bound is analogous to one that holds when the vect…
Spectral Methods for Learning Multivariate Latent Tree Structure
Animashree Anandkumar, Kamalika Chaudhuri, Daniel Hsu +3
This work considers the problem of learning the structure of multivariate linear tree models, which include a variety of directed tree graphical models with continuous, discrete, a…
Robust Matrix Decomposition with Outliers
Daniel Hsu, Sham M. Kakade, Tong Zhang
Suppose a given observation matrix can be decomposed as the sum of a low-rank matrix and a sparse matrix (outliers), and the goal is to recover these individual components from the…