Guang Cheng - RankMe

126 papers A* 19A 12Journal 91Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Alignment of Diffusion Model and Flow Matching for Text-to-Image Generation. CoRR Yidong Ouyang, Liyan Xie, Hongyuan Zha, Guang Cheng
2026	J	`jnl`	Authenticated Contradictions from Desynchronized Provenance and Watermarking. CoRR Alexander Nemecek, Hengzhi He, Guang Cheng, Erman Ayday
2026	J	`jnl`	Corrected Samplers for Discrete Flow Models. CoRR Zhengyan Wan, Yidong Ouyang, Liyan Xie, Fang Fang, Hongyuan Zha, Guang Cheng
2026	J	`jnl`	Enhancing TableQA through Verifiable Reasoning Trace Reward. CoRR Tung Sum Thomas Kwok, Xinyu Wang, Hengzhi He, Xiaofeng Lin, Peng Lu, Liheng Ma, Chunhe Wang, Ying Nian Wu, Lei Ding, Guang Cheng
2026	J	`jnl`	Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting. CoRR Joshua Ward, Chi-hua Wang, Guang Cheng
2026	J	`jnl`	Training-Free Self-Correction for Multimodal Masked Diffusion Models. CoRR Yidong Ouyang, Panwen Hu, Zhengyan Wan, Zhe Wang, Liyan Xie, Dmitriy Bespalov, Ying Nian Wu, Guang Cheng, Hongyuan Zha, Qiang Sun
2025	J	`jnl`	A Probabilistic Perspective on Model Collapse. CoRR Shirong Xu, Hengzhi He, Guang Cheng
2025	A*	`conf`	CTSyn: A Foundation Model for Cross Tabular Data Generation. ICLR Xiaofeng Lin, Chenheng Xu, Matthew Yang, Guang Cheng
2025	J	`jnl`	Decentralized Sparse Linear Regression via Gradient-Tracking. J. Mach. Learn. Res. Marie Maros, Gesualdo Scutari, Ying Sun, Guang Cheng
2025	J	`jnl`	Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies. J. Mach. Learn. Res. Zhanyu Wang, Guang Cheng, Jordan Awan
2025	J	`jnl`	Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching. CoRR Zhengyan Wan, Yidong Ouyang, Liyan Xie, Fang Fang, Hongyuan Zha, Guang Cheng
2025	—	`conf`	Ensembling Membership Inference Attacks Against Tabular Generative Models. AISec@CCS Joshua Ward, Yuxuan Yang, Chi-hua Wang, Guang Cheng
2025	J	`jnl`	Ensembling Membership Inference Attacks Against Tabular Generative Models. CoRR Joshua Ward, Yuxuan Yang, Chi-hua Wang, Guang Cheng
2025	J	`jnl`	Error Analysis of Discrete Flow with Generator Matching. CoRR Zhengyan Wan, Yidong Ouyang, Qiang Yao, Liyan Xie, Fang Fang, Hongyuan Zha, Guang Cheng
2025	A	`conf`	Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval. CIKM Yuxiang Liu, Tian Wang, Gourab Kundu, Tianyu Cao, Guang Cheng, Zhen Ge, Jianshu Chen, Qingjun Cui, Trishul Chilimbi
2025	J	`jnl`	Golden Ratio Mixing of Real and Synthetic Data for Stabilizing Generative Model Training. CoRR Hengzhi He, Shirong Xu, Guang Cheng
2025	J	`jnl`	Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary. J. Mach. Learn. Res. Tianyang Hu, Ruiqi Liu, Zuofeng Shang, Guang Cheng
2025	J	`jnl`	Privacy Auditing Synthetic Data Release through Local Likelihood Attacks. CoRR Joshua Ward, Chi-hua Wang, Guang Cheng
2025	J	`jnl`	Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation. CoRR Jessup Byun, Xiaofeng Lin, Joshua Ward, Guang Cheng
2025	J	`jnl`	Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis. CoRR Joshua Ward, Xiaofeng Lin, Chi-hua Wang, Guang Cheng
2025	J	`jnl`	TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data. Trans. Mach. Learn. Res. Namjoon Suh, Yuning Yang, Din-Yin Hsieh, Qitong Luan, Shirong Xu, Shixiang Zhu, Guang Cheng
2025	J	`jnl`	When Tables Leak: Attacking String Memorization in LLM-Based Tabular Data Generation. CoRR Joshua Ward, Bochao Gu, Chi-hua Wang, Guang Cheng
2024	J	`jnl`	A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models. CoRR Namjoon Suh, Guang Cheng
2024	J	`jnl`	Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data. CoRR Yu Xia, Chi-hua Wang, Joshua Mabry, Guang Cheng
2024	J	`jnl`	Approximation of RKHS Functionals by Neural Networks. CoRR Tian-Yi Zhou, Namjoon Suh, Guang Cheng, Xiaoming Huo
2024	J	`jnl`	BadGD: A unified data-centric framework to identify gradient descent vulnerabilities. CoRR Chi-hua Wang, Guang Cheng
2024	J	`jnl`	Benefits of Transformer: In-Context Learning in Linear Regression Tasks with Unstructured Data. CoRR Yue Xing, Xiaofeng Lin, Namjoon Suh, Qifan Song, Guang Cheng
2024	A	`conf`	Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective. AISTATS Yue Xing, Xiaofeng Lin, Qifan Song, Yi Xu, Belinda Zeng, Guang Cheng
2024	J	`jnl`	Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective. CoRR Yue Xing, Xiaofeng Lin, Qifan Song, Yi Xu, Belinda Zeng, Guang Cheng
2024	J	`jnl`	CTSyn: A Foundational Model for Cross Tabular Data Generation. CoRR Xiaofeng Lin, Chenheng Xu, Matthew Yang, Guang Cheng
2024	J	`jnl`	Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models. CoRR Joshua Ward, Chi-hua Wang, Guang Cheng
2024	J	`jnl`	Discriminative Estimation of Total Variation Distance: A Fidelity Auditor for Generative Data. CoRR Lan Tao, Shirong Xu, Chi-hua Wang, Namjoon Suh, Guang Cheng
2024	J	`jnl`	Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models. CoRR Yinan Cheng, Chi-hua Wang, Vamsi K. Potluru, Tucker Balch, Guang Cheng
2024	J	`jnl`	Dynamic Online Recommendation for Two-Sided Market with Bayesian Incentive Compatibility. CoRR Yuantong Li, Guang Cheng, Xiaowu Dai
2024	A	`conf`	FairRR: Pre-Processing for Group Fairness through Randomized Response. AISTATS Joshua John Ward, Xianli Zeng, Guang Cheng
2024	J	`jnl`	FairRR: Pre-Processing for Group Fairness through Randomized Response. CoRR Xianli Zeng, Joshua Ward, Guang Cheng
2024	J	`jnl`	Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective. CoRR Din-Yin Hsieh, Chi-hua Wang, Guang Cheng
2024	J	`jnl`	Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space. CoRR Peiyu Yu, Dinghuai Zhang, Hengzhi He, Xiaojian Ma, Ruiyao Miao, Yifan Lu, Yasi Zhang, Deqian Kong, Ruiqi Gao, Jianwen Xie, Guang Cheng, Ying Nian Wu
2024	J	`jnl`	Rate-Optimal Rank Aggregation with Private Pairwise Rankings. CoRR Shirong Xu, Will Wei Sun, Guang Cheng
2024	J	`jnl`	TimeAutoDiff: Combining Autoencoder and Diffusion model for time series tabular data synthesizing. CoRR Namjoon Suh, Yuning Yang, Din-Yin Hsieh, Qitong Luan, Shirong Xu, Shixiang Zhu, Guang Cheng
2024	A*	`conf`	Transfer Learning for Diffusion Models. NeurIPS Yidong Ouyang, Liyan Xie, Hongyuan Zha, Guang Cheng
2024	J	`jnl`	Transfer Learning for Diffusion Models. CoRR Yidong Ouyang, Liyan Xie, Hongyuan Zha, Guang Cheng
2024	A*	`conf`	Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints. ICML Yuantong Li, Guang Cheng, Xiaowu Dai
2024	J	`jnl`	Watermarking Generative Categorical Data. CoRR Bochao Gu, Hengzhi He, Guang Cheng
2024	J	`jnl`	Watermarking Generative Tabular Data. CoRR Hengzhi He, Peiyu Yu, Junpeng Ren, Ying Nian Wu, Guang Cheng
2023	J	`jnl`	AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing. CoRR Namjoon Suh, Xiaofeng Lin, Din-Yin Hsieh, Merhdad Honarkhah, Guang Cheng
2023	J	`jnl`	Binary Classification under Local Label Differential Privacy Using Randomized Response Mechanisms. Trans. Mach. Learn. Res. Shirong Xu, Chendi Wang, Will Wei Sun, Guang Cheng
2023	J	`jnl`	Double Matching Under Complementary Preferences. CoRR Yuantong Li, Guang Cheng, Xiaowu Dai
2023	A*	`conf`	Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process. ICML Yidong Ouyang, Liyan Xie, Guang Cheng
2023	J	`jnl`	MissDiff: Training Diffusion Models on Tabular Data with Missing Values. CoRR Yidong Ouyang, Liyan Xie, Chongxuan Li, Guang Cheng
2023	J	`jnl`	Optimal Convergence Rates of Deep Convolutional Neural Networks: Additive Ridge Functions. Trans. Mach. Learn. Res. Zhiying Fang, Guang Cheng
2023	J	`jnl`	Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization. Trans. Mach. Learn. Res. Wenjie Li, Chi-hua Wang, Guang Cheng, Qifan Song
2023	J	`jnl`	Ranking Differential Privacy. CoRR Shirong Xu, Will Wei Sun, Guang Cheng
2023	A*	`conf`	Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms. ICLR Ximing Li, Chendi Wang, Guang Cheng
2023	J	`jnl`	Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms. CoRR Ximing Li, Chendi Wang, Guang Cheng
2023	J	`jnl`	Utility Theory of Synthetic Data Generation. CoRR Shirong Xu, Will Wei Sun, Guang Cheng
2022	J	`jnl`	Attention Enables Zero Approximation Error. CoRR Zhiying Fang, Yidong Ouyang, Ding-Xuan Zhou, Guang Cheng
2022	J	`jnl`	Benefit of Interpolation in Nearest Neighbor Algorithms. CoRR Yue Xing, Qifan Song, Guang Cheng
2022	J	`jnl`	Benefit of Interpolation in Nearest Neighbor Algorithms. SIAM J. Math. Data Sci. Yue Xing, Qifan Song, Guang Cheng
2022	J	`jnl`	Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies. CoRR Zhanyu Wang, Guang Cheng, Jordan Awan
2022	J	`jnl`	Distributed Bootstrap for Simultaneous Inference Under High Dimensionality. J. Mach. Learn. Res. Yang Yu, Shih-Kang Chao, Guang Cheng
2022	J	`jnl`	Enhanced Nearest Neighbor Classification for Crowdsourcing. CoRR Jiexin Duan, Xingye Qiao, Guang Cheng
2022	J	`jnl`	High-Dimensional Inference over Networks: Linear Convergence and Statistical Guarantees. CoRR Ying Sun, Marie Maros, Gesualdo Scutari, Guang Cheng
2022	J	`jnl`	Improving Adversarial Robustness by Contrastive Guided Diffusion Process. CoRR Yidong Ouyang, Liyan Xie, Guang Cheng
2022	J	`jnl`	On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation. CoRR Yucong Liu, Chi-hua Wang, Guang Cheng
2022	J	`jnl`	Optimal Learning Rates of Deep Convolutional Neural Networks: Additive Ridge Functions. CoRR Zhiying Fang, Guang Cheng
2022	A*	`conf`	Phase Transition from Clean Training to Adversarial Training. NeurIPS Yue Xing, Qifan Song, Guang Cheng
2022	J	`jnl`	Rate-Optimal Contextual Online Matching Bandit. CoRR Yuantong Li, Chi-hua Wang, Guang Cheng, Will Wei Sun
2022	J	`jnl`	Residual Bootstrap Exploration for Stochastic Linear Bandit. CoRR Shuang Wu, Chi-hua Wang, Yuantong Li, Guang Cheng
2022	A	`conf`	Residual bootstrap exploration for stochastic linear bandit. UAI Shuang Wu, Chi-hua Wang, Yuantong Li, Guang Cheng
2022	A	`conf`	Unlabeled Data Help: Minimax Analysis and Adversarial Robustness. AISTATS Yue Xing, Qifan Song, Guang Cheng
2022	J	`jnl`	Unlabeled Data Help: Minimax Analysis and Adversarial Robustness. CoRR Yue Xing, Qifan Song, Guang Cheng
2022	J	`jnl`	Variance reduction on general adaptive stochastic mirror descent. Mach. Learn. Wenjie Li, Zhanyu Wang, Yichen Zhang, Guang Cheng
2022	A*	`conf`	Why Do Artificially Generated Data Help Adversarial Robustness. NeurIPS Yue Xing, Qifan Song, Guang Cheng
2021	A	`conf`	Adversarially Robust Estimate and Risk Analysis in Linear Regression. AISTATS Yue Xing, Ruizhi Zhang, Guang Cheng
2021	A*	`conf`	On the Algorithmic Stability of Adversarial Training. NeurIPS Yue Xing, Qifan Song, Guang Cheng
2021	A	`conf`	On the Generalization Properties of Adversarial Training. AISTATS Yue Xing, Qifan Song, Guang Cheng
2021	J	`jnl`	Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning. CoRR Pratik Ramprasad, Yuantong Li, Zhuoran Yang, Zhaoran Wang, Will Wei Sun, Guang Cheng
2021	A	`conf`	Online Forgetting Process for Linear Regression Models. AISTATS Yuantong Li, Chi-hua Wang, Guang Cheng
2021	J	`jnl`	Optimum-statistical collaboration towards efficient black-box optimization. CoRR Wenjie Li, Chi-hua Wang, Guang Cheng
2021	A	`conf`	Predictive Power of Nearest Neighbors Algorithm under Random Perturbation. AISTATS Yue Xing, Qifan Song, Guang Cheng
2021	A	`conf`	Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network. AISTATS Tianyang Hu, Wenjia Wang, Cong Lin, Guang Cheng
2020	J	`jnl`	Adversarially Robust Estimate and Risk Analysis in Linear Regression. CoRR Yue Xing, Ruizhi Zhang, Guang Cheng
2020	A*	`conf`	Directional Pruning of Deep Neural Networks. NeurIPS Shih-Kang Chao, Zhanyu Wang, Yue Xing, Guang Cheng
2020	J	`jnl`	Directional Pruning of Deep Neural Networks. CoRR Shih-Kang Chao, Zhanyu Wang, Yue Xing, Guang Cheng
2020	A*	`conf`	Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee. NeurIPS Jincheng Bai, Qifan Song, Guang Cheng
2020	J	`jnl`	Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee. CoRR Jincheng Bai, Qifan Song, Guang Cheng
2020	A*	`conf`	Mutual Transfer Learning for Massive Data. ICML Ching-Wei Cheng, Xingye Qiao, Guang Cheng
2020	J	`jnl`	Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors. CoRR Jincheng Bai, Qifan Song, Guang Cheng
2020	A*	`conf`	Non-asymptotic Analysis for Nonparametric Testing. COLT Yun Yang, Zuofeng Shang, Guang Cheng
2020	J	`jnl`	On Deep Instrumental Variables Estimate. CoRR Ruiqi Liu, Zuofeng Shang, Guang Cheng
2020	J	`jnl`	On the Generalization Properties of Adversarial Training. CoRR Yue Xing, Qifan Song, Guang Cheng
2020	A	`conf`	Online Batch Decision-Making with High-Dimensional Covariates. AISTATS Chi-hua Wang, Guang Cheng
2020	J	`jnl`	Online Batch Decision-Making with High-Dimensional Covariates. CoRR Chi-hua Wang, Guang Cheng
2020	J	`jnl`	Online Forgetting Process for Linear Regression Models. CoRR Yuantong Li, Chi-hua Wang, Guang Cheng
2020	J	`jnl`	Online Regularization for High-Dimensional Dynamic Pricing Algorithms. CoRR Chi-hua Wang, Zhanyu Wang, Will Wei Sun, Guang Cheng
2020	J	`jnl`	Optimal Rate of Convergence for Deep Neural Network Classifiers under the Teacher-Student Setting. CoRR Tianyang Hu, Zuofeng Shang, Guang Cheng
2020	J	`jnl`	Predictive Power of Nearest Neighbors Algorithm under Random Perturbation. CoRR Yue Xing, Qifan Song, Guang Cheng
2020	J	`jnl`	Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network. CoRR Wenjia Wang, Tianyang Hu, Cong Lin, Guang Cheng
2020	J	`jnl`	Residual Bootstrap Exploration for Bandit Algorithms. CoRR Chi-hua Wang, Yang Yu, Botao Hao, Guang Cheng
2020	A*	`conf`	Simultaneous Inference for Massive Data: Distributed Bootstrap. ICML Yang Yu, Shih-Kang Chao, Guang Cheng
2020	J	`jnl`	Simultaneous Inference for Massive Data: Distributed Bootstrap. CoRR Yang Yu, Shih-Kang Chao, Guang Cheng
2020	J	`jnl`	Sparse and Low-Rank Tensor Estimation via Cubic Sketchings. IEEE Trans. Inf. Theory Botao Hao, Anru Zhang, Guang Cheng
2020	A	`conf`	Sparse and Low-rank Tensor Estimation via Cubic Sketchings. AISTATS Botao Hao, Anru R. Zhang, Guang Cheng
2020	A*	`conf`	Statistical Guarantees of Distributed Nearest Neighbor Classification. NeurIPS Jiexin Duan, Xingye Qiao, Guang Cheng
2020	J	`jnl`	Tensor Graphical Model: Non-Convex Optimization and Statistical Inference. IEEE Trans. Pattern Anal. Mach. Intell. Xiang Lyu, Will Wei Sun, Zhaoran Wang, Han Liu, Jian Yang, Guang Cheng
2020	J	`jnl`	Variance Reduction on Adaptive Stochastic Mirror Descent. CoRR Wenjie Li, Zhanyu Wang, Yichen Zhang, Guang Cheng
2019	J	`jnl`	A generalization of regularized dual averaging and its dynamics. CoRR Shih-Kang Chao, Guang Cheng
2019	J	`jnl`	Benefit of Interpolation in Nearest Neighbor Algorithms. CoRR Yue Xing, Qifan Song, Guang Cheng
2019	A*	`conf`	Bootstrapping Upper Confidence Bound. NeurIPS Botao Hao, Yasin Abbasi-Yadkori, Zheng Wen, Guang Cheng
2019	J	`jnl`	Bootstrapping Upper Confidence Bound. CoRR Botao Hao, Yasin Abbasi-Yadkori, Zheng Wen, Guang Cheng
2019	J	`jnl`	Nonparametric Bayesian Aggregation for Massive Data. J. Mach. Learn. Res. Zuofeng Shang, Botao Hao, Guang Cheng
2019	A*	`conf`	Rates of Convergence for Large-scale Nearest Neighbor Classification. NeurIPS Xingye Qiao, Jiexin Duan, Guang Cheng
2019	J	`jnl`	Rates of Convergence for Large-scale Nearest Neighbor Classification. CoRR Xingye Qiao, Jiexin Duan, Guang Cheng
2019	A*	`conf`	Sharp Theoretical Analysis for Nonparametric Testing under Random Projection. COLT Meimei Liu, Zuofeng Shang, Guang Cheng
2018	A*	`conf`	Early Stopping for Nonparametric Testing. NeurIPS Meimei Liu, Guang Cheng
2018	A*	`conf`	Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data. ICML Ganggang Xu, Zuofeng Shang, Guang Cheng
2018	J	`jnl`	Statistical Optimality of Interpolated Nearest Neighbor Algorithms. CoRR Yue Xing, Qifan Song, Guang Cheng
2018	—	`conf`	Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression. Allerton Meimei Liu, Jean Honorio, Guang Cheng
2018	J	`jnl`	Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression. CoRR Meimei Liu, Jean Honorio, Guang Cheng
2018	J	`jnl`	Stein Neural Sampler. CoRR Tianyang Hu, Zixiang Chen, Hanxi Sun, Jincheng Bai, Mao Ye, Guang Cheng
2017	J	`jnl`	Computational Limits of A Distributed Algorithm for Smoothing Spline. J. Mach. Learn. Res. Zuofeng Shang, Guang Cheng
2017	J	`jnl`	Simultaneous Clustering and Estimation of Heterogeneous Graphical Models. J. Mach. Learn. Res. Botao Hao, Will Wei Sun, Yufeng Liu, Guang Cheng
2016	—	`conf`	Analysing the Semantic Change Based on Word Embedding. NLPCC/ICCPOL Xuanyi Liao, Guang Cheng
2015	—	`conf`	Non-convex Statistical Optimization for Sparse Tensor Graphical Model. NIPS Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng
2014	J	`jnl`	Nearest Neighbor Classifier with Optimal Stability. CoRR Wei Sun, Xingye Qiao, Guang Cheng