
Optimumstatistical collaboration towards efficient blackbox optimization
With increasingly more hyperparameters involved in their training, machi...
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Distributed Bootstrap for Simultaneous Inference Under High Dimensionality
We propose a distributed bootstrap method for simultaneous inference on ...
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Variance Reduction on Adaptive Stochastic Mirror Descent
We study the idea of variance reduction applied to adaptive stochastic m...
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Power Iteration for Tensor PCA
In this paper, we study the power iteration algorithm for the spiked ten...
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Unionnet: A deep neural network model adapted to small data sets
In real applications, generally small data sets can be obtained. At pres...
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Adversarially Robust Estimate and Risk Analysis in Linear Regression
Adversarially robust learning aims to design algorithms that are robust ...
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Online Forgetting Process for Linear Regression Models
Motivated by the EU's "Right To Be Forgotten" regulation, we initiate a ...
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Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee
Sparse deep learning aims to address the challenge of huge storage consu...
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Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors
We propose a variational Bayesian (VB) procedure for highdimensional li...
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Sparse Confidence Sets for Normal Mean Models
In this paper, we propose a new framework to construct confidence sets f...
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On the Generalization Properties of Adversarial Training
Modern machine learning and deep learning models are shown to be vulnera...
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Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network
Overparametrized neural networks trained by gradient descent (GD) can pr...
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Online Regularization for HighDimensional Dynamic Pricing Algorithms
We propose a novel online regularization scheme for revenuemaximization...
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Directional Pruning of Deep Neural Networks
In the light of the fact that the stochastic gradient descent (SGD) ofte...
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On Deep Instrumental Variables Estimate
The endogeneity issue is fundamentally important as many empirical appli...
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Online Batch DecisionMaking with HighDimensional Covariates
We propose and investigate a class of new algorithms for sequential deci...
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Simultaneous Inference for Massive Data: Distributed Bootstrap
In this paper, we propose a bootstrap method applied to massive data pro...
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Residual Bootstrap Exploration for Bandit Algorithms
In this paper, we propose a novel perturbationbased exploration method ...
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Predictive Power of Nearest Neighbors Algorithm under Random Perturbation
We consider a data corruption scenario in the classical k Nearest Neighb...
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Optimal Rate of Convergence for Deep Neural Network Classifiers under the TeacherStudent Setting
Classifiers built with neural networks handle largescale highdimension...
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Rate Optimal Variational Bayesian Inference for Sparse DNN
Sparse deep neural network (DNN) has drawn much attention in recent stud...
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Benefit of Interpolation in Nearest Neighbor Algorithms
The overparameterized models attract much attention in the era of data ...
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A generalization of regularized dual averaging and its dynamics
Excessive computational cost for learning large data and streaming data ...
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Machine Learning in/for Blockchain: Future and Challenges
Machine learning (including deep and reinforcement learning) and blockch...
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Rates of Convergence for Largescale Nearest Neighbor Classification
Nearest neighbor is a popular class of classification methods with many ...
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Bootstrapping Upper Confidence Bound
Upper Confidence Bound (UCB) method is arguably the most celebrated one ...
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Enhancing Multimodel Inference with Natural Selection
Multimodel inference covers a wide range of modern statistical applicat...
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An Efficient Network Intrusion Detection System Based on Feature Selection and Ensemble Classifier
Since Internet is so popular and prevailing in human life, countering cy...
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Bayesian Fusion Estimation via tShrinkage
Shrinkage prior has gained great successes in many data analysis, howeve...
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Optimal False Discovery Control of Minimax Estimator
In the analysis of high dimensional regression models, there are two imp...
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Distributed Nearest Neighbor Classification
Nearest neighbor is a popular nonparametric method for classification an...
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Finite Time Analysis of Vector Autoregressive Models under Linear Restrictions
This paper develops a unified finitetime theory for the OLS estimation ...
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High Dimensional Robust Inference for Cox Regression Models
We consider highdimensional inference for potentially misspecified Cox ...
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Stein Neural Sampler
We propose two novel samplers to produce highquality samples from a giv...
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Statistical Optimality of Interpolated Nearest Neighbor Algorithms
In the era of deep learning, understanding overfitting phenomenon becom...
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ModerateDimensional Inferences on Quadratic Functionals in Ordinary Least Squares
Statistical inferences on quadratic functionals of linear regression par...
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Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression
In this paper, we propose a random projection approach to estimate varia...
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Quadratic Discriminant Analysis under Moderate Dimension
Quadratic discriminant analysis (QDA) is a simple method to classify a s...
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Early Stopping for Nonparametric Testing
Early stopping of iterative algorithms is an algorithmic regularization ...
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How Many Machines Can We Use in Parallel Computing for Kernel Ridge Regression?
This paper attempts to solve a basic problem in distributed statistical ...
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Nonparametric Testing under Random Projection
A common challenge in nonparametric inference is its high computational ...
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Sparse and Lowrank Tensor Estimation via Cubic Sketchings
In this paper, we propose a general framework for sparse and lowrank te...
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Stability Enhanced LargeMargin Classifier Selection
Stability is an important aspect of a classification procedure because u...
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Sparse Tensor Graphical Model: Nonconvex Optimization and Statistical Inference
We consider the estimation and inference of sparse graphical models that...
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Provable Sparse Tensor Decomposition
We propose a novel sparse tensor decomposition method, namely Tensor Tru...
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Stabilized Nearest Neighbor Classifier and Its Statistical Properties
The stability of statistical analysis is an important indicator for repr...
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Local and global asymptotic inference in smoothing spline models
This article studies local and global inference for smoothing spline est...
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Guang Cheng
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