Cheng, Guang

54 publications

ICLR 2025 CTSyn: A Foundation Model for Cross Tabular Data Generation Xiaofeng Lin, Chenheng Xu, Matthew Yang, Guang Cheng

JMLR 2025 Decentralized Sparse Linear Regression via Gradient-Tracking Marie Maros, Gesualdo Scutari, Ying Sun, Guang Cheng

JMLR 2025 Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies Zhanyu Wang, Guang Cheng, Jordan Awan

AAAI 2025 Dual-Channel Interactive Graph Transformer for Traffic Classification with Message-Aware Flow Representation Xing Qiu, Guang Cheng, Weizhou Zhu, Dandan Niu, Nan Fu

NeurIPS 2025 FlowRefiner: A Robust Traffic Classification Framework Against Label Noise Mingwei Zhan, Ruijie Zhao, Xianwen Deng, Zhi Xue, Qi Li, Zhuotao Liu, Guang Cheng, Ke Xu

JMLR 2025 Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary Tianyang Hu, Ruiqi Liu, Zuofeng Shang, Guang Cheng

TMLR 2025 TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data Namjoon Suh, Yuning Yang, Din-Yin Hsieh, Qitong Luan, Shirong Xu, Shixiang Zhu, Guang Cheng

AISTATS 2024 Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective Yue Xing, Xiaofeng Lin, Qifan Song, Yi Xu, Belinda Zeng, Guang Cheng

AISTATS 2024 FairRR: Pre-Processing for Group Fairness Through Randomized Response Joshua John Ward, Xianli Zeng, Guang Cheng

NeurIPS 2024 Transfer Learning for Diffusion Models Yidong Ouyang, Liyan Xie, Hongyuan Zha, Guang Cheng

ICML 2024 Two-Sided Competing Matching Recommendation Markets with Quota and Complementary Preferences Constraints Yuantong Li, Guang Cheng, Xiaowu Dai

NeurIPSW 2023 AutoDiff: Combining Auto-Encoder and Diffusion Model for Tabular Data Synthesizing Namjoon Suh, Xiaofeng Lin, Din-Yin Hsieh, Mehrdad Honarkhah, Guang Cheng

TMLR 2023 Binary Classification Under Local Label Differential Privacy Using Randomized Response Mechanisms Shirong Xu, Chendi Wang, Will Wei Sun, Guang Cheng

ICML 2023 Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process Yidong Ouyang, Liyan Xie, Guang Cheng

ICMLW 2023 MissDiff: Training Diffusion Models on Tabular Data with Missing Values Yidong Ouyang, Liyan Xie, Chongxuan Li, Guang Cheng

TMLR 2023 Optimal Convergence Rates of Deep Convolutional Neural Networks: Additive Ridge Functions Zhiying Fang, Guang Cheng

TMLR 2023 Optimum-Statistical Collaboration Towards General and Efficient Black-Box Optimization Wenjie Li, Chi-Hua Wang, Guang Cheng, Qifan Song

ICLR 2023 Statistical Theory of Differentially Private Marginal-Based Data Synthesis Algorithms Ximing Li, Chendi Wang, Guang Cheng

AISTATS 2022 Unlabeled Data Help: Minimax Analysis and Adversarial Robustness Yue Xing, Qifan Song, Guang Cheng

JMLR 2022 Distributed Bootstrap for Simultaneous Inference Under High Dimensionality Yang Yu, Shih-Kang Chao, Guang Cheng

NeurIPS 2022 Fair Bayes-Optimal Classifiers Under Predictive Parity Xianli Zeng, Edgar Dobriban, Guang Cheng

NeurIPS 2022 Phase Transition from Clean Training to Adversarial Training Yue Xing, Qifan Song, Guang Cheng

JMLR 2022 Power Iteration for Tensor PCA Jiaoyang Huang, Daniel Z. Huang, Qing Yang, Guang Cheng

UAI 2022 Residual Bootstrap Exploration for Stochastic Linear Bandit Shuang Wu, Chi-Hua Wang, Yuantong Li, Guang Cheng

MLJ 2022 Variance Reduction on General Adaptive Stochastic Mirror Descent Wenjie Li, Zhanyu Wang, Yichen Zhang, Guang Cheng

NeurIPS 2022 Why Do Artificially Generated Data Help Adversarial Robustness Yue Xing, Qifan Song, Guang Cheng

AISTATS 2021 Adversarially Robust Estimate and Risk Analysis in Linear Regression Yue Xing, Ruizhi Zhang, Guang Cheng

AISTATS 2021 On the Generalization Properties of Adversarial Training Yue Xing, Qifan Song, Guang Cheng

AISTATS 2021 Online Forgetting Process for Linear Regression Models Yuantong Li, Chi-Hua Wang, Guang Cheng

AISTATS 2021 Predictive Power of Nearest Neighbors Algorithm Under Random Perturbation Yue Xing, Qifan Song, Guang Cheng

AISTATS 2021 Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network Tianyang Hu, Wenjia Wang, Cong Lin, Guang Cheng

NeurIPS 2021 On the Algorithmic Stability of Adversarial Training Yue Xing, Qifan Song, Guang Cheng

NeurIPS 2020 Directional Pruning of Deep Neural Networks Shih-Kang Chao, Zhanyu Wang, Yue Xing, Guang Cheng

NeurIPS 2020 Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee Jincheng Bai, Qifan Song, Guang Cheng

ICML 2020 Mutual Transfer Learning for Massive Data Ching-Wei Cheng, Xingye Qiao, Guang Cheng

COLT 2020 Non-Asymptotic Analysis for Nonparametric Testing Yun Yang, Zuofeng Shang, Guang Cheng

AISTATS 2020 Online Batch Decision-Making with High-Dimensional Covariates Chi-Hua Wang, Guang Cheng

ICML 2020 Simultaneous Inference for Massive Data: Distributed Bootstrap Yang Yu, Shih-Kang Chao, Guang Cheng

AISTATS 2020 Sparse and Low-Rank Tensor Estimation via Cubic Sketchings Botao Hao, Anru R. Zhang, Guang Cheng

NeurIPS 2020 Statistical Guarantees of Distributed Nearest Neighbor Classification Jiexin Duan, Xingye Qiao, Guang Cheng

NeurIPS 2019 Bootstrapping Upper Confidence Bound Botao Hao, Yasin Abbasi Yadkori, Zheng Wen, Guang Cheng

AISTATS 2019 High Dimensional Inference in Partially Linear Models Ying Zhu, Zhuqing Yu, Guang Cheng

JMLR 2019 Nonparametric Bayesian Aggregation for Massive Data Zuofeng Shang, Botao Hao, Guang Cheng

NeurIPS 2019 Rates of Convergence for Large-Scale Nearest Neighbor Classification Xingye Qiao, Jiexin Duan, Guang Cheng

COLT 2019 Sharp Theoretical Analysis for Nonparametric Testing Under Random Projection Meimei Liu, Zuofeng Shang, Guang Cheng

NeurIPS 2018 Early Stopping for Nonparametric Testing Meimei Liu, Guang Cheng

ICML 2018 Optimal Tuning for Divide-and-Conquer Kernel Ridge Regression with Massive Data Ganggang Xu, Zuofeng Shang, Guang Cheng

JMLR 2017 Computational Limits of a Distributed Algorithm for Smoothing Spline Zuofeng Shang, Guang Cheng

NeurIPS 2015 Non-Convex Statistical Optimization for Sparse Tensor Graphical Model Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng

JMLR 2015 Optimal Bayesian Estimation in Random Covariate Design with a Rescaled Gaussian Process Prior Debdeep Pati, Anirban Bhattacharya, Guang Cheng

CVPR 2014 Tracking on the Product Manifold of Shape and Orientation for Tractography from Diffusion MRI Yuanxiang Wang, Hesamoddin Salehian, Guang Cheng, Baba C. Vemuri

ICCV 2013 Recursive Estimation of the Stein Center of SPD Matrices and Its Applications Hesamoddin Salehian, Guang Cheng, Baba C. Vemuri, Jeffrey Ho

AISTATS 2013 Recursive Karcher Expectation Estimators and Geometric Law of Large Numbers Jeffrey Ho, Guang Cheng, Hesamoddin Salehian, Baba C. Vemuri

ECCV 2012 Efficient Recursive Algorithms for Computing the Mean Diffusion Tensor and Applications to DTI Segmentation Guang Cheng, Hesamoddin Salehian, Baba C. Vemuri