Nearest Neighbor Classifier with Optimal Stability. - Wei Sun 027; Xingye Qiao; Guang Cheng 003 (CoRR, 2014)

Non-convex Statistical Optimization for Sparse Tensor Graphical Model. - Wei Sun 027; Zhaoran Wang 001; Han Liu 001; Guang Cheng 003 (NIPS, 2015)

Analysing the Semantic Change Based on Word Embedding. - Xuanyi Liao; Guang Cheng 003 (NLPCC/ICCPOL, 2016)

Simultaneous Clustering and Estimation of Heterogeneous Graphical Models. - Botao Hao; Will Wei Sun; Yufeng Liu; Guang Cheng 003 (J. Mach. Learn. Res., 2017)

Computational Limits of A Distributed Algorithm for Smoothing Spline. - Zuofeng Shang; Guang Cheng 003 (J. Mach. Learn. Res., 2017)

Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression. - Meimei Liu 002; Jean Honorio; Guang Cheng 003 (Allerton, 2018)

Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data. - Ganggang Xu; Zuofeng Shang; Guang Cheng 003 (ICML, 2018)

Early Stopping for Nonparametric Testing. - Meimei Liu 002; Guang Cheng 003 (NeurIPS, 2018)

Statistical Optimality of Interpolated Nearest Neighbor Algorithms. - Yue Xing 002; Qifan Song; Guang Cheng 003 (CoRR, 2018)

Sharp Theoretical Analysis for Nonparametric Testing under Random Projection. - Meimei Liu 002; Zuofeng Shang; Guang Cheng 003 (COLT, 2019)

Bootstrapping Upper Confidence Bound. - Botao Hao; Yasin Abbasi-Yadkori; Zheng Wen 002; Guang Cheng 003 (NeurIPS, 2019)

Rates of Convergence for Large-scale Nearest Neighbor Classification. - Xingye Qiao; Jiexin Duan; Guang Cheng 003 (NeurIPS, 2019)

A generalization of regularized dual averaging and its dynamics. - Shih-Kang Chao; Guang Cheng 003 (CoRR, 2019)

Nonparametric Bayesian Aggregation for Massive Data. - Zuofeng Shang; Botao Hao; Guang Cheng 003 (J. Mach. Learn. Res., 2019)

Sparse and Low-rank Tensor Estimation via Cubic Sketchings. - Botao Hao; Anru R. Zhang; Guang Cheng 003 (AISTATS, 2020)

Online Batch Decision-Making with High-Dimensional Covariates. - Chi-Hua Wang; Guang Cheng 003 (AISTATS, 2020)

Non-asymptotic Analysis for Nonparametric Testing. - Yun Yang; Zuofeng Shang; Guang Cheng 003 (COLT, 2020)

Mutual Transfer Learning for Massive Data. - Ching-Wei Cheng; Xingye Qiao; Guang Cheng 003 (ICML, 2020)

Simultaneous Inference for Massive Data: Distributed Bootstrap. - Yang Yu 052; Shih-Kang Chao; Guang Cheng 003 (ICML, 2020)

Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee. - Jincheng Bai; Qifan Song; Guang Cheng 003 (NeurIPS, 2020)

Directional Pruning of Deep Neural Networks. - Shih-Kang Chao; Zhanyu Wang; Yue Xing 002; Guang Cheng 003 (NeurIPS, 2020)

Statistical Guarantees of Distributed Nearest Neighbor Classification. - Jiexin Duan; Xingye Qiao; Guang Cheng 003 (NeurIPS, 2020)

Optimal Rate of Convergence for Deep Neural Network Classifiers under the Teacher-Student Setting. - Tianyang Hu; Zuofeng Shang; Guang Cheng 003 (CoRR, 2020)

Residual Bootstrap Exploration for Bandit Algorithms. - Chi-Hua Wang; Yang Yu 052; Botao Hao; Guang Cheng 003 (CoRR, 2020)

On Deep Instrumental Variables Estimate. - Ruiqi Liu; Zuofeng Shang; Guang Cheng 003 (CoRR, 2020)

Online Regularization for High-Dimensional Dynamic Pricing Algorithms. - Chi-Hua Wang; Zhanyu Wang; Will Wei Sun; Guang Cheng 003 (CoRR, 2020)

Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors. - Jincheng Bai; Qifan Song; Guang Cheng 003 (CoRR, 2020)

Variance Reduction on Adaptive Stochastic Mirror Descent. - Wenjie Li; Zhanyu Wang; Yichen Zhang; Guang Cheng 003 (CoRR, 2020)

Tensor Graphical Model: Non-Convex Optimization and Statistical Inference. - Xiang Lyu; Will Wei Sun; Zhaoran Wang 001; Han Liu 001; Jian Yang 002; Guang Cheng 003 (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Online Forgetting Process for Linear Regression Models. - Yuantong Li; Chi-Hua Wang; Guang Cheng 003 (AISTATS, 2021)

Predictive Power of Nearest Neighbors Algorithm under Random Perturbation. - Yue Xing 002; Qifan Song; Guang Cheng 003 (AISTATS, 2021)

On the Generalization Properties of Adversarial Training. - Yue Xing 002; Qifan Song; Guang Cheng 003 (AISTATS, 2021)

Adversarially Robust Estimate and Risk Analysis in Linear Regression. - Yue Xing 002; Ruizhi Zhang; Guang Cheng 003 (AISTATS, 2021)

On the Algorithmic Stability of Adversarial Training. - Yue Xing 002; Qifan Song; Guang Cheng 003 (NeurIPS, 2021)

Optimum-statistical collaboration towards efficient black-box optimization. - Wenjie Li; Chi-Hua Wang; Guang Cheng 003 (CoRR, 2021)

Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning. - Pratik Ramprasad; Yuantong Li; Zhuoran Yang; Zhaoran Wang 001; Will Wei Sun; Guang Cheng 003 (CoRR, 2021)

Unlabeled Data Help: Minimax Analysis and Adversarial Robustness. - Yue Xing 002; Qifan Song; Guang Cheng 003 (AISTATS, 2022)

Why Do Artificially Generated Data Help Adversarial Robustness. - Yue Xing 002; Qifan Song; Guang Cheng 003 (NeurIPS, 2022)

Phase Transition from Clean Training to Adversarial Training. - Yue Xing 002; Qifan Song; Guang Cheng 003 (NeurIPS, 2022)

Residual bootstrap exploration for stochastic linear bandit. - Shuang Wu; Chi-Hua Wang; Yuantong Li; Guang Cheng 003 (UAI, 2022)

Optimal Learning Rates of Deep Convolutional Neural Networks: Additive Ridge Functions. - Zhiying Fang; Guang Cheng 003 (CoRR, 2022)

Attention Enables Zero Approximation Error. - Zhiying Fang; Yidong Ouyang; Ding-Xuan Zhou; Guang Cheng 003 (CoRR, 2022)

Enhanced Nearest Neighbor Classification for Crowdsourcing. - Jiexin Duan; Xingye Qiao; Guang Cheng 003 (CoRR, 2022)

Rate-Optimal Contextual Online Matching Bandit. - Yuantong Li; Chi-Hua Wang; Guang Cheng 003; Will Wei Sun (CoRR, 2022)

Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies. - Zhanyu Wang; Guang Cheng 003; Jordan Awan (CoRR, 2022)

Improving Adversarial Robustness by Contrastive Guided Diffusion Process. - Yidong Ouyang; Liyan Xie; Guang Cheng 003 (CoRR, 2022)

On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation. - Yucong Liu; Chi-Hua Wang; Guang Cheng 003 (CoRR, 2022)

Distributed Bootstrap for Simultaneous Inference Under High Dimensionality. - Yang Yu 052; Shih-Kang Chao; Guang Cheng 003 (J. Mach. Learn. Res., 2022)

Variance reduction on general adaptive stochastic mirror descent. - Wenjie Li; Zhanyu Wang; Yichen Zhang; Guang Cheng 003 (Mach. Learn., 2022)

Benefit of Interpolation in Nearest Neighbor Algorithms. - Yue Xing 002; Qifan Song; Guang Cheng 003 (SIAM J. Math. Data Sci., 2022)

Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms. - Ximing Li; Chendi Wang; Guang Cheng 003 (ICLR, 2023)

Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process. - Yidong Ouyang; Liyan Xie; Guang Cheng 003 (ICML, 2023)

Ranking Differential Privacy. - Shirong Xu; Will Wei Sun; Guang Cheng 003 (CoRR, 2023)

Double Matching Under Complementary Preferences. - Yuantong Li; Guang Cheng 003; Xiaowu Dai (CoRR, 2023)

Utility Theory of Synthetic Data Generation. - Shirong Xu; Will Wei Sun; Guang Cheng 003 (CoRR, 2023)

MissDiff: Training Diffusion Models on Tabular Data with Missing Values. - Yidong Ouyang; Liyan Xie; Chongxuan Li; Guang Cheng 003 (CoRR, 2023)

AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing. - Namjoon Suh; Xiaofeng Lin 005; Din-Yin Hsieh; Merhdad Honarkhah; Guang Cheng 003 (CoRR, 2023)

Optimal Convergence Rates of Deep Convolutional Neural Networks: Additive Ridge Functions. - Zhiying Fang; Guang Cheng 003 (Trans. Mach. Learn. Res., 2023)

Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization. - Wenjie Li; Chi-Hua Wang; Guang Cheng 003; Qifan Song (Trans. Mach. Learn. Res., 2023)

Binary Classification under Local Label Differential Privacy Using Randomized Response Mechanisms. - Shirong Xu; Chendi Wang; Will Wei Sun; Guang Cheng 003 (Trans. Mach. Learn. Res., 2023)

Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective. - Yue Xing 002; Xiaofeng Lin 005; Qifan Song; Yi Xu 011; Belinda Zeng; Guang Cheng 003 (AISTATS, 2024)

FairRR: Pre-Processing for Group Fairness through Randomized Response. - Joshua John Ward; Xianli Zeng; Guang Cheng 003 (AISTATS, 2024)

Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints. - Yuantong Li; Guang Cheng 003; Xiaowu Dai (ICML, 2024)

Transfer Learning for Diffusion Models. - Yidong Ouyang; Liyan Xie; Hongyuan Zha; Guang Cheng 003 (NeurIPS, 2024)

Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective. - Din-Yin Hsieh; Chi-Hua Wang; Guang Cheng 003 (CoRR, 2024)

Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models. - Yinan Cheng; Chi-Hua Wang; Vamsi K. Potluru; Tucker Balch; Guang Cheng 003 (CoRR, 2024)

A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models. - Namjoon Suh; Guang Cheng 003 (CoRR, 2024)

Benefits of Transformer: In-Context Learning in Linear Regression Tasks with Unstructured Data. - Yue Xing 002; Xiaofeng Lin 005; Namjoon Suh; Qifan Song; Guang Cheng 003 (CoRR, 2024)

Rate-Optimal Rank Aggregation with Private Pairwise Rankings. - Shirong Xu; Will Wei Sun; Guang Cheng 003 (CoRR, 2024)

Approximation of RKHS Functionals by Neural Networks. - Tian-Yi Zhou 009; Namjoon Suh; Guang Cheng 003; Xiaoming Huo (CoRR, 2024)

Watermarking Generative Tabular Data. - Hengzhi He; Peiyu Yu; Junpeng Ren; Ying Nian Wu; Guang Cheng 003 (CoRR, 2024)

Discriminative Estimation of Total Variation Distance: A Fidelity Auditor for Generative Data. - Lan Tao; Shirong Xu; Chi-Hua Wang; Namjoon Suh; Guang Cheng 003 (CoRR, 2024)

BadGD: A unified data-centric framework to identify gradient descent vulnerabilities. - Chi-Hua Wang; Guang Cheng 003 (CoRR, 2024)

Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space. - Peiyu Yu; Dinghuai Zhang; Hengzhi He; Xiaojian Ma 001; Ruiyao Miao; Yifan Lu; Yasi Zhang; Deqian Kong; Ruiqi Gao; Jianwen Xie; Guang Cheng 003; Ying Nian Wu (CoRR, 2024)

Dynamic Online Recommendation for Two-Sided Market with Bayesian Incentive Compatibility. - Yuantong Li; Guang Cheng 003; Xiaowu Dai (CoRR, 2024)

CTSyn: A Foundational Model for Cross Tabular Data Generation. - Xiaofeng Lin 005; Chenheng Xu; Matthew Yang; Guang Cheng 003 (CoRR, 2024)

Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models. - Joshua Ward; Chi-Hua Wang; Guang Cheng 003 (CoRR, 2024)

Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data. - Yu Xia; Chi-Hua Wang; Joshua Mabry; Guang Cheng 003 (CoRR, 2024)

TimeAutoDiff: Combining Autoencoder and Diffusion model for time series tabular data synthesizing. - Namjoon Suh; Yuning Yang; Din-Yin Hsieh; Qitong Luan; Shirong Xu; Shixiang Zhu; Guang Cheng 003 (CoRR, 2024)

Watermarking Generative Categorical Data. - Bochao Gu; Hengzhi He; Guang Cheng 003 (CoRR, 2024)

CTSyn: A Foundation Model for Cross Tabular Data Generation. - Xiaofeng Lin 005; Chenheng Xu; Matthew Yang; Guang Cheng 003 (ICLR, 2025)

Golden Ratio Mixing of Real and Synthetic Data for Stabilizing Generative Model Training. - Hengzhi He; Shirong Xu; Guang Cheng 003 (CoRR, 2025)

A Probabilistic Perspective on Model Collapse. - Shirong Xu; Hengzhi He; Guang Cheng 003 (CoRR, 2025)

Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation. - Jessup Byun; Xiaofeng Lin 005; Joshua Ward; Guang Cheng 003 (CoRR, 2025)

Privacy Auditing Synthetic Data Release through Local Likelihood Attacks. - Joshua Ward; Chi-Hua Wang; Guang Cheng 003 (CoRR, 2025)

Ensembling Membership Inference Attacks Against Tabular Generative Models. - Joshua Ward; Yuxuan Yang; Chi-Hua Wang; Guang Cheng 003 (CoRR, 2025)

Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis. - Joshua Ward; Xiaofeng Lin 005; Chi-Hua Wang; Guang Cheng 003 (CoRR, 2025)

Error Analysis of Discrete Flow with Generator Matching. - Zhengyan Wan; Yidong Ouyang; Qiang Yao; Liyan Xie; Fang Fang; Hongyuan Zha; Guang Cheng 003 (CoRR, 2025)

Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching. - Zhengyan Wan; Yidong Ouyang; Liyan Xie; Fang Fang; Hongyuan Zha; Guang Cheng 003 (CoRR, 2025)