b'Guang Cheng'

research

∙ 09/16/2023

Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule

As a modern ensemble technique, Deep Forest (DF) employs a cascading str...

0 Hongyu Zhu, et al. ∙

research

∙ 07/02/2023

MissDiff: Training Diffusion Models on Tabular Data with Missing Values

The diffusion model has shown remarkable performance in modeling data di...

0 Yidong Ouyang, et al. ∙

research

∙ 05/17/2023

Utility Theory of Synthetic Data Generation

Evaluating the utility of synthetic data is critical for measuring the e...

0 Shirong Xu, et al. ∙

research

∙ 03/13/2023

Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Norm

In non-asymptotic statistical inferences, variance-type parameters of su...

0 Huiming Zhang, et al. ∙

research

∙ 01/24/2023

Double Matching Under Complementary Preferences

In this paper, we propose a new algorithm for addressing the problem of ...

0 Yuantong Li, et al. ∙

research

∙ 01/21/2023

Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms

Marginal-based methods achieve promising performance in the synthetic da...

0 Ximing Li, et al. ∙

research

∙ 01/02/2023

Ranking Differential Privacy

Rankings are widely collected in various real-life scenarios, leading to...

0 Shirong Xu, et al. ∙

research

∙ 11/28/2022

On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation

Devising procedures for auditing generative model privacy-utility tradeo...

0 Yucong Liu, et al. ∙

research

∙ 10/18/2022

Improving Adversarial Robustness by Contrastive Guided Diffusion Process

Synthetic data generation has become an emerging tool to help improve th...

0 Yidong Ouyang, et al. ∙

research

∙ 10/12/2022

Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies

Differential private (DP) mechanisms protect individual-level informatio...

0 Zhanyu Wang, et al. ∙

research

∙ 07/04/2022

Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary

Deep learning has gained huge empirical successes in large-scale classif...

0 Tianyang Hu, et al. ∙

research

∙ 05/15/2022

Fair Bayes-Optimal Classifiers Under Predictive Parity

Increasing concerns about disparate effects of AI have motivated a great...

0 Xianli Zeng, et al. ∙

research

∙ 05/07/2022

Rate-Optimal Contextual Online Matching Bandit

Two-sided online matching platforms have been employed in various market...

1 Yuantong Li, et al. ∙

research

∙ 02/27/2022

Federated Online Sparse Decision Making

This paper presents a novel federated linear contextual bandits model, w...

0 Chi-Hua Wang, et al. ∙

research

∙ 02/26/2022

Enhanced Nearest Neighbor Classification for Crowdsourcing

In machine learning, crowdsourcing is an economical way to label a large...

0 Jiexin Duan, et al. ∙

research

∙ 02/24/2022

Attention Enables Zero Approximation Error

Deep learning models have been widely applied in various aspects of dail...

0 Zhiying Fang, et al. ∙

research

∙ 02/24/2022

Optimal Learning Rates of Deep Convolutional Neural Networks: Additive Ridge Functions

Convolutional neural networks have shown extraordinary abilities in many...

0 Zhiying Fang, et al. ∙

research

∙ 02/23/2022

Residual Bootstrap Exploration for Stochastic Linear Bandit

We propose a new bootstrap-based online algorithm for stochastic linear ...

1 Shuang Wu, et al. ∙

research

∙ 02/20/2022

Bayes-Optimal Classifiers under Group Fairness

Machine learning algorithms are becoming integrated into more and more h...

0 Xianli Zeng, et al. ∙

research

∙ 02/14/2022

Unlabeled Data Help: Minimax Analysis and Adversarial Robustness

The recent proposed self-supervised learning (SSL) approaches successful...

0 Yue Xing, et al. ∙

research

∙ 01/21/2022

High-Dimensional Inference over Networks: Linear Convergence and Statistical Guarantees

We study sparse linear regression over a network of agents, modeled as a...

0 Ying Sun, et al. ∙

research

∙ 08/20/2021

A Quantitative Framework for Network Resilience Evaluation using Dynamic Bayesian Network

Measuring and evaluating network resilience has become an important aspe...

0 Shanqing Jiang, et al. ∙

research

∙ 08/08/2021

Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning

The recent emergence of reinforcement learning has created a demand for ...

5 Pratik Ramprasad, et al. ∙

research

∙ 06/17/2021

Optimum-statistical collaboration towards efficient black-box optimization

With increasingly more hyperparameters involved in their training, machi...

0 Wenjie Li, et al. ∙

research

∙ 02/19/2021

Distributed Bootstrap for Simultaneous Inference Under High Dimensionality

We propose a distributed bootstrap method for simultaneous inference on ...

0 Yang Yu, et al. ∙

research

∙ 12/26/2020

Variance Reduction on Adaptive Stochastic Mirror Descent

We study the idea of variance reduction applied to adaptive stochastic m...

0 Wenjie Li, et al. ∙

research

∙ 12/26/2020

Power Iteration for Tensor PCA

In this paper, we study the power iteration algorithm for the spiked ten...

0 Jiaoyang Huang, et al. ∙

research

∙ 12/24/2020

Union-net: A deep neural network model adapted to small data sets

In real applications, generally small data sets can be obtained. At pres...

0 Qingfang He, et al. ∙

research

∙ 12/18/2020

Adversarially Robust Estimate and Risk Analysis in Linear Regression

Adversarially robust learning aims to design algorithms that are robust ...

0 Yue Xing, et al. ∙

research

∙ 12/03/2020

Online Forgetting Process for Linear Regression Models

Motivated by the EU's "Right To Be Forgotten" regulation, we initiate a ...

4 Yuantong Li, et al. ∙

research

∙ 11/15/2020

Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee

Sparse deep learning aims to address the challenge of huge storage consu...

0 Jincheng Bai, et al. ∙

research

∙ 10/24/2020

Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors

We propose a variational Bayesian (VB) procedure for high-dimensional li...

0 Jincheng Bai, et al. ∙

research

∙ 08/17/2020

Sparse Confidence Sets for Normal Mean Models

In this paper, we propose a new framework to construct confidence sets f...

0 Yang Ning, et al. ∙

research

∙ 08/15/2020

On the Generalization Properties of Adversarial Training

Modern machine learning and deep learning models are shown to be vulnera...

0 Yue Xing, et al. ∙

research

∙ 07/06/2020

Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network

Overparametrized neural networks trained by gradient descent (GD) can pr...

0 Wenjia Wang, et al. ∙

research

∙ 07/05/2020

Online Regularization for High-Dimensional Dynamic Pricing Algorithms

We propose a novel online regularization scheme for revenue-maximization...

0 Chi-Hua Wang, et al. ∙

research

∙ 06/16/2020

Directional Pruning of Deep Neural Networks

In the light of the fact that the stochastic gradient descent (SGD) ofte...

0 Shih-Kang Chao, et al. ∙

research

∙ 04/30/2020

On Deep Instrumental Variables Estimate

The endogeneity issue is fundamentally important as many empirical appli...

0 Ruiqi Liu, et al. ∙

research

∙ 02/21/2020

Online Batch Decision-Making with High-Dimensional Covariates

We propose and investigate a class of new algorithms for sequential deci...

0 Chi-Hua Wang, et al. ∙

research

∙ 02/19/2020

Simultaneous Inference for Massive Data: Distributed Bootstrap

In this paper, we propose a bootstrap method applied to massive data pro...

0 Yang Yu, et al. ∙

research

∙ 02/19/2020

Residual Bootstrap Exploration for Bandit Algorithms

In this paper, we propose a novel perturbation-based exploration method ...

0 Chi-Hua Wang, et al. ∙

research

∙ 02/13/2020

Predictive Power of Nearest Neighbors Algorithm under Random Perturbation

We consider a data corruption scenario in the classical k Nearest Neighb...

0 Yue Xing, et al. ∙

research

∙ 01/19/2020

Optimal Rate of Convergence for Deep Neural Network Classifiers under the Teacher-Student Setting

Classifiers built with neural networks handle large-scale high-dimension...

0 Tianyang Hu, et al. ∙

research

∙ 10/10/2019

Rate Optimal Variational Bayesian Inference for Sparse DNN

Sparse deep neural network (DNN) has drawn much attention in recent stud...

0 Jincheng Bai, et al. ∙

research

∙ 09/25/2019

Benefit of Interpolation in Nearest Neighbor Algorithms

The over-parameterized models attract much attention in the era of data ...

0 Yue Xing, et al. ∙

research

∙ 09/22/2019

A generalization of regularized dual averaging and its dynamics

Excessive computational cost for learning large data and streaming data ...

0 Shih-Kang Chao, et al. ∙

research

∙ 09/12/2019

Machine Learning in/for Blockchain: Future and Challenges

Machine learning (including deep and reinforcement learning) and blockch...

0 Fang Chen, et al. ∙

research

∙ 09/03/2019

Rates of Convergence for Large-scale Nearest Neighbor Classification

Nearest neighbor is a popular class of classification methods with many ...

0 Xingye Qiao, et al. ∙

research

∙ 06/12/2019

Bootstrapping Upper Confidence Bound

Upper Confidence Bound (UCB) method is arguably the most celebrated one ...

0 Botao Hao, et al. ∙

research

∙ 06/06/2019

Enhancing Multi-model Inference with Natural Selection

Multi-model inference covers a wide range of modern statistical applicat...

0 Ching-Wei Cheng, et al. ∙

Guang Cheng

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