
PartitionMallows Model and Its Inference for Rank Aggregation
Learning how to aggregate ranking lists has been an active research area...
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Power of FDR Control Methods: The Impact of Ranking Algorithm, Tampered Design, and Symmetric Statistic
As the power of FDR control methods for highdimensional variable select...
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Neural Gaussian Mirror for Controlled Feature Selection in Neural Networks
Deep neural networks (DNNs) have become increasingly popular and achieve...
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Measurement error models: from nonparametric methods to deep neural networks
The success of deep learning has inspired recent interests in applying n...
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A Scalefree Approach for False Discovery Rate Control in Generalized Linear Models
The generalized linear models (GLM) have been widely used in practice to...
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On Posterior Consistency of Bayesian Factor Models in High Dimensions
As a principled dimension reduction technique, factor models have been w...
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Scalable Uncertainty Quantification via GenerativeBootstrap Sampler
It has been believed that the virtue of using statistical procedures is ...
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Stratification and Optimal Resampling for Sequential Monte Carlo
Sequential Monte Carlo (SMC), also known as particle filters, has been w...
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False Discovery Rate Control via Data Splitting
Selecting relevant features associated with a given response variable is...
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Controlling False Discovery Rate Using Gaussian Mirrors
Simultaneously finding multiple influential variables and controlling th...
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Minimax Nonparametric Twosample Test
We consider the problem of comparing probability densities between two g...
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Monte Carlo Approximation of Bayes Factors via Mixing with Surrogate Distributions
By mixing the posterior distribution with a surrogate distribution, of w...
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The WangLandau Algorithm as Stochastic Optimization and its Acceleration
We show that the WangLandau algorithm can be formulated as a stochastic...
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Generative Parameter Sampler For Scalable Uncertainty Quantification
Uncertainty quantification has been a core of the statistical machine le...
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Sentence Segmentation for Classical Chinese Based on LSTM with Radical Embedding
In this paper, we develop a low than character feature embedding called ...
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IMMIGRATE: A Marginbased Feature Selection Method with Interaction Terms
By balancing marginquantity maximization and marginquality maximizatio...
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Neuronized Priors for Bayesian Sparse Linear Regression
Although Bayesian variable selection procedures have been widely adopted...
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Bayesian Hidden Markov Tree Models for Clustering Genes with Shared Evolutionary History
Determination of functions for poorly characterized genes is crucial for...
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Randomization Inference for Peer Effects
Many previous causal inference studies require no interference among uni...
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Global testing under the sparse alternatives for single index models
For the single index model y=f(β^τx,ϵ) with Gaussian design, and β is a...
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L1Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs
It is known that for a certain class of single index models (SIMs) Y = f...
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Signed Support Recovery for Single Index Models in HighDimensions
In this paper we study the support recovery problem for single index mod...
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A Unified Theory of Confidence Regions and Testing for High Dimensional Estimating Equations
We propose a new inferential framework for constructing confidence regio...
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Interpretable Selection and Visualization of Features and Interactions Using Bayesian Forests
It is becoming increasingly important for machine learning methods to ma...
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Jun S. Liu
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Professor of Statistics, Director of Graduate Studies at Harvard University