Guang Cheng

PAGPL: Privacy-Aware Graph Prompt Learning Scheme via Adaptive Perturbation-Estimated Topology Recovery AAAI 2026 MPAS: Breaking Sequential Constraints of Multi-Agent Communication Topologies via Individual-Epistemic Message Propagation AAAI 2026 Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies JMLR 2025 Decentralized Sparse Linear Regression via Gradient-Tracking JMLR 2025 Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary JMLR 2025 Dual-Channel Interactive Graph Transformer for Traffic Classification with Message-Aware Flow Representation AAAI 2025 CTSyn: A Foundation Model for Cross Tabular Data Generation ICLR 2025 From Address Blocks to Authorized Prefixes: Redesigning RPKI ROV with a Hierarchical Hashing Scheme for Fast and Memory-Efficient Validation NSDI 2025 FairRR: Pre-Processing for Group Fairness through Randomized Response AISTATS 2024 Transfer Learning for Diffusion Models NIPS 2024 Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective AISTATS 2024 Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints ICML 2024 Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms ICLR 2023 Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process ICML 2023 Why Do Artificially Generated Data Help Adversarial Robustness NIPS 2022 Unlabeled Data Help: Minimax Analysis and Adversarial Robustness AISTATS 2022 Power Iteration for Tensor PCA JMLR 2022 Distributed Bootstrap for Simultaneous Inference Under High Dimensionality JMLR 2022 Phase Transition from Clean Training to Adversarial Training NIPS 2022 Fair Bayes-Optimal Classifiers Under Predictive Parity NIPS 2022 Residual bootstrap exploration for stochastic linear bandit UAI 2022 Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network AISTATS 2021 On the Algorithmic Stability of Adversarial Training NIPS 2021 Online Forgetting Process for Linear Regression Models AISTATS 2021 Predictive Power of Nearest Neighbors Algorithm under Random Perturbation AISTATS 2021 On the Generalization Properties of Adversarial Training AISTATS 2021 Adversarially Robust Estimate and Risk Analysis in Linear Regression AISTATS 2021 Statistical Guarantees of Distributed Nearest Neighbor Classification NIPS 2020 Non-asymptotic Analysis for Nonparametric Testing COLT 2020 Mutual Transfer Learning for Massive Data ICML 2020 Simultaneous Inference for Massive Data: Distributed Bootstrap ICML 2020 Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee NIPS 2020 Sparse and Low-rank Tensor Estimation via Cubic Sketchings AISTATS 2020 Directional Pruning of Deep Neural Networks NIPS 2020 Online Batch Decision-Making with High-Dimensional Covariates AISTATS 2020 Rates of Convergence for Large-scale Nearest Neighbor Classification NIPS 2019 Nonparametric Bayesian Aggregation for Massive Data JMLR 2019 Sharp Theoretical Analysis for Nonparametric Testing under Random Projection COLT 2019 High Dimensional Inference in Partially Linear Models AISTATS 2019 Bootstrapping Upper Confidence Bound NIPS 2019 Simultaneous Clustering and Estimation of Heterogeneous Graphical Models JMLR 2018 Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data ICML 2018 Early Stopping for Nonparametric Testing NIPS 2018 Computational Limits of A Distributed Algorithm for Smoothing Spline JMLR 2017 Optimal Bayesian Estimation in Random Covariate Design with a Rescaled Gaussian Process Prior JMLR 2015 Non-convex Statistical Optimization for Sparse Tensor Graphical Model NIPS 2015 Tracking on the Product Manifold of Shape and Orientation for Tractography from Diffusion MRI CVPR 2014 Recursive Karcher Expectation Estimators And Geometric Law of Large Numbers AISTATS 2013 Recursive Estimation of the Stein Center of SPD Matrices and Its Applications ICCV 2013