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09/16/2023
Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule
As a modern ensemble technique, Deep Forest (DF) employs a cascading str...
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07/02/2023
MissDiff: Training Diffusion Models on Tabular Data with Missing Values
The diffusion model has shown remarkable performance in modeling data di...
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05/17/2023
Utility Theory of Synthetic Data Generation
Evaluating the utility of synthetic data is critical for measuring the e...
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03/13/2023
Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Norm
In non-asymptotic statistical inferences, variance-type parameters of su...
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01/24/2023
Double Matching Under Complementary Preferences
In this paper, we propose a new algorithm for addressing the problem of ...
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01/21/2023
Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms
Marginal-based methods achieve promising performance in the synthetic da...
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01/02/2023
Ranking Differential Privacy
Rankings are widely collected in various real-life scenarios, leading to...
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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...
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10/18/2022
Improving Adversarial Robustness by Contrastive Guided Diffusion Process
Synthetic data generation has become an emerging tool to help improve th...
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10/12/2022
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies
Differential private (DP) mechanisms protect individual-level informatio...
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07/04/2022
Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary
Deep learning has gained huge empirical successes in large-scale classif...
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05/15/2022
Fair Bayes-Optimal Classifiers Under Predictive Parity
Increasing concerns about disparate effects of AI have motivated a great...
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05/07/2022
Rate-Optimal Contextual Online Matching Bandit
Two-sided online matching platforms have been employed in various market...
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02/27/2022
Federated Online Sparse Decision Making
This paper presents a novel federated linear contextual bandits model, w...
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02/26/2022
Enhanced Nearest Neighbor Classification for Crowdsourcing
In machine learning, crowdsourcing is an economical way to label a large...
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02/24/2022
Attention Enables Zero Approximation Error
Deep learning models have been widely applied in various aspects of dail...
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02/24/2022
Optimal Learning Rates of Deep Convolutional Neural Networks: Additive Ridge Functions
Convolutional neural networks have shown extraordinary abilities in many...
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02/23/2022
Residual Bootstrap Exploration for Stochastic Linear Bandit
We propose a new bootstrap-based online algorithm for stochastic linear ...
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02/20/2022
Bayes-Optimal Classifiers under Group Fairness
Machine learning algorithms are becoming integrated into more and more h...
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02/14/2022
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness
The recent proposed self-supervised learning (SSL) approaches successful...
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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...
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08/20/2021
A Quantitative Framework for Network Resilience Evaluation using Dynamic Bayesian Network
Measuring and evaluating network resilience has become an important aspe...
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08/08/2021
Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning
The recent emergence of reinforcement learning has created a demand for ...
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06/17/2021
Optimum-statistical collaboration towards efficient black-box optimization
With increasingly more hyperparameters involved in their training, machi...
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02/19/2021
Distributed Bootstrap for Simultaneous Inference Under High Dimensionality
We propose a distributed bootstrap method for simultaneous inference on ...
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12/26/2020
Variance Reduction on Adaptive Stochastic Mirror Descent
We study the idea of variance reduction applied to adaptive stochastic m...
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12/26/2020
Power Iteration for Tensor PCA
In this paper, we study the power iteration algorithm for the spiked ten...
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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...
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12/18/2020
Adversarially Robust Estimate and Risk Analysis in Linear Regression
Adversarially robust learning aims to design algorithms that are robust ...
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12/03/2020
Online Forgetting Process for Linear Regression Models
Motivated by the EU's "Right To Be Forgotten" regulation, we initiate a ...
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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...
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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...
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08/17/2020
Sparse Confidence Sets for Normal Mean Models
In this paper, we propose a new framework to construct confidence sets f...
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08/15/2020
On the Generalization Properties of Adversarial Training
Modern machine learning and deep learning models are shown to be vulnera...
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07/06/2020
Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network
Overparametrized neural networks trained by gradient descent (GD) can pr...
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07/05/2020
Online Regularization for High-Dimensional Dynamic Pricing Algorithms
We propose a novel online regularization scheme for revenue-maximization...
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06/16/2020
Directional Pruning of Deep Neural Networks
In the light of the fact that the stochastic gradient descent (SGD) ofte...
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04/30/2020
On Deep Instrumental Variables Estimate
The endogeneity issue is fundamentally important as many empirical appli...
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02/21/2020
Online Batch Decision-Making with High-Dimensional Covariates
We propose and investigate a class of new algorithms for sequential deci...
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02/19/2020
Simultaneous Inference for Massive Data: Distributed Bootstrap
In this paper, we propose a bootstrap method applied to massive data pro...
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02/19/2020
Residual Bootstrap Exploration for Bandit Algorithms
In this paper, we propose a novel perturbation-based exploration method ...
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02/13/2020
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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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...
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10/10/2019
Rate Optimal Variational Bayesian Inference for Sparse DNN
Sparse deep neural network (DNN) has drawn much attention in recent stud...
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09/25/2019
Benefit of Interpolation in Nearest Neighbor Algorithms
The over-parameterized models attract much attention in the era of data ...
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09/22/2019
A generalization of regularized dual averaging and its dynamics
Excessive computational cost for learning large data and streaming data ...
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09/12/2019
Machine Learning in/for Blockchain: Future and Challenges
Machine learning (including deep and reinforcement learning) and blockch...
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09/03/2019
Rates of Convergence for Large-scale Nearest Neighbor Classification
Nearest neighbor is a popular class of classification methods with many ...
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06/12/2019
Bootstrapping Upper Confidence Bound
Upper Confidence Bound (UCB) method is arguably the most celebrated one ...
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06/06/2019