b'Jun S. Liu'

research

∙ 08/29/2023

Multi-Response Heteroscedastic Gaussian Process Models and Their Inference

Despite the widespread utilization of Gaussian process models for versat...

0 Taehee Lee, et al. ∙

research

∙ 07/06/2023

On the Optimality of Functional Sliced Inverse Regression

In this paper, we prove that functional sliced inverse regression (FSIR)...

0 Rui Chen, et al. ∙

research

∙ 07/04/2023

Monotone Cubic B-Splines

We present a method for fitting monotone curves using cubic B-splines wi...

0 Lijun Wang, et al. ∙

research

∙ 01/25/2022

Varying Coefficient Model via Adaptive Spline Fitting

The varying coefficient model has received wide attention from researche...

0 Xufei Wang, et al. ∙

research

∙ 12/16/2021

On Gibbs Sampling for Structured Bayesian Models Discussion of paper by Zanella and Roberts

This article is a discussion of Zanella and Roberts' paper: Multilevel l...

0 Xiaodong Yang, et al. ∙

research

∙ 11/30/2021

Convergence Rate of Multiple-try Metropolis Independent sampler

The Multiple-try Metropolis (MTM) method is an interesting extension of ...

0 Xiaodong Yang, et al. ∙

research

∙ 10/28/2021

Kernel-based Partial Permutation Test for Detecting Heterogeneous Functional Relationship

We propose a kernel-based partial permutation test for checking the equa...

0 Xinran Li, et al. ∙

research

∙ 04/15/2021

Partition-Mallows Model and Its Inference for Rank Aggregation

Learning how to aggregate ranking lists has been an active research area...

0 Wanchuang Zhu, et al. ∙

research

∙ 10/16/2020

Power of FDR Control Methods: The Impact of Ranking Algorithm, Tampered Design, and Symmetric Statistic

As the power of FDR control methods for high-dimensional variable select...

0 Zheng Tracy Ke, et al. ∙

research

∙ 10/13/2020

Neural Gaussian Mirror for Controlled Feature Selection in Neural Networks

Deep neural networks (DNNs) have become increasingly popular and achieve...

1 Xin Xing, et al. ∙

research

∙ 07/15/2020

Measurement error models: from nonparametric methods to deep neural networks

The success of deep learning has inspired recent interests in applying n...

0 Zhirui Hu, et al. ∙

research

∙ 07/02/2020

A Scale-free Approach for False Discovery Rate Control in Generalized Linear Models

The generalized linear models (GLM) have been widely used in practice to...

0 Chenguang Dai, et al. ∙

research

∙ 06/01/2020

On Posterior Consistency of Bayesian Factor Models in High Dimensions

As a principled dimension reduction technique, factor models have been w...

0 Yucong Ma, et al. ∙

research

∙ 06/01/2020

Scalable Uncertainty Quantification via GenerativeBootstrap Sampler

It has been believed that the virtue of using statistical procedures is ...

0 Minsuk Shin, et al. ∙

research

∙ 04/04/2020

Stratification and Optimal Resampling for Sequential Monte Carlo

Sequential Monte Carlo (SMC), also known as particle filters, has been w...

0 Yichao Li, et al. ∙

research

∙ 02/20/2020

False Discovery Rate Control via Data Splitting

Selecting relevant features associated with a given response variable is...

0 Chenguang Dai, et al. ∙

research

∙ 11/21/2019

Controlling False Discovery Rate Using Gaussian Mirrors

Simultaneously finding multiple influential variables and controlling th...

0 Xin Xing, et al. ∙

research

∙ 11/06/2019

Minimax Nonparametric Two-sample Test

We consider the problem of comparing probability densities between two g...

9 Xin Xing, et al. ∙

research

∙ 09/12/2019

Monte Carlo Approximation of Bayes Factors via Mixing with Surrogate Distributions

By mixing the posterior distribution with a surrogate distribution, of w...

0 Chenguang Dai, et al. ∙

research

∙ 07/27/2019

The Wang-Landau Algorithm as Stochastic Optimization and its Acceleration

We show that the Wang-Landau algorithm can be formulated as a stochastic...

0 Chenguang Dai, et al. ∙

research

∙ 05/28/2019

Generative Parameter Sampler For Scalable Uncertainty Quantification

Uncertainty quantification has been a core of the statistical machine le...

0 Minsuk Shin, et al. ∙

research

∙ 10/05/2018

Sentence Segmentation for Classical Chinese Based on LSTM with Radical Embedding

In this paper, we develop a low than character feature embedding called ...

0 Xu Han, et al. ∙

research

∙ 10/05/2018

IMMIGRATE: A Margin-based Feature Selection Method with Interaction Terms

By balancing margin-quantity maximization and margin-quality maximizatio...

0 Ruzhang Zhao, et al. ∙

research

∙ 09/29/2018

Neuronized Priors for Bayesian Sparse Linear Regression

Although Bayesian variable selection procedures have been widely adopted...

0 Minsuk Shin, et al. ∙

research

∙ 08/18/2018

Bayesian Hidden Markov Tree Models for Clustering Genes with Shared Evolutionary History

Determination of functions for poorly characterized genes is crucial for...

0 Yang Li, et al. ∙

research

∙ 07/04/2018

Randomization Inference for Peer Effects

Many previous causal inference studies require no interference among uni...

0 Xinran Li, et al. ∙

research

∙ 05/04/2018

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...

0 Qian Lin, et al. ∙

research

∙ 11/25/2015

L1-Regularized 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...

0 Matey Neykov, et al. ∙

research

∙ 11/07/2015

Signed Support Recovery for Single Index Models in High-Dimensions

In this paper we study the support recovery problem for single index mod...

0 Matey Neykov, et al. ∙

research

∙ 10/30/2015

A Unified Theory of Confidence Regions and Testing for High Dimensional Estimating Equations

We propose a new inferential framework for constructing confidence regio...

0 Matey Neykov, et al. ∙

research

∙ 06/08/2015

Interpretable Selection and Visualization of Features and Interactions Using Bayesian Forests

It is becoming increasingly important for machine learning methods to ma...

0 Viktoriya Krakovna, et al. ∙

Jun S. Liu

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