← Authors

Guang Cheng

49 papers · 2013–2026 · 11 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (11) πŸƒ Academic Marathon (12) πŸ—ΊοΈ Taxonomy Completionist (22) 🌈 Renaissance Researcher (7) 🧭 Keyword Pioneer 🐝 Cross-Pollinator (5) 🐣 Hot Topic Early Bird 🧬 Topic Evolution πŸ† Keyword Champion (2) πŸ† Grand Slam πŸ”¬ Deep Specialist (17) πŸ’Ž Century Club (47) πŸ—ƒοΈ Keyword Collector (53) πŸš€ Conference Pioneer ⚑ Prolific Year (5) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (9)

Conferences

AISTATS (12) NIPS (12) JMLR (9) ICML (5) AAAI (3) COLT (2) ICLR (2) CVPR (1) ICCV (1) NSDI (1) UAI (1)

Top co-authors

Yue Xing (9) Qifan Song (8) Zuofeng Shang (6) Botao Hao (4) Xingye Qiao (3) Yuantong Li (3) Chi-Hua Wang (3) Hesamoddin Salehian (3) Shih-Kang Chao (3) Zhanyu Wang (2)

Research topics

Statistics (3) Differential Privacy (1)

Keywords

adversarial training (7) distributed learning (6) statistical inference (5) adversarial robustness (5) regret bound (4) nonparametric testing (3) kernel ridge regression (3) neural network (3) high-dimensional inference (3) online learning (3) nearest neighbor classification (2) linear regression (2) early stopping (2) reproducing kernel hilbert space (2) hypothesis testing (2) gradient descent (2) domain adaptation (2) transfer learning (2) message passing (2) minimax optimality (2)

Papers

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