Guang Cheng

Orcid: 0000-0002-7874-9404

Affiliations:
  • University of California Los Angeles, Department of Statistics, CA, USA
  • Purdue University, Department of Statistics, West Lafayette, IN, USA


According to our database1, Guang Cheng authored at least 46 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Approximation of RKHS Functionals by Neural Networks.
CoRR, 2024

Rate-Optimal Rank Aggregation with Private Pairwise Rankings.
CoRR, 2024

Benefits of Transformer: In-Context Learning in Linear Regression Tasks with Unstructured Data.
CoRR, 2024

Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective.
CoRR, 2024

A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models.
CoRR, 2024

Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models.
CoRR, 2024

Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective.
CoRR, 2024

2023
AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing.
CoRR, 2023

Utility Theory of Synthetic Data Generation.
CoRR, 2023

Double Matching Under Complementary Preferences.
CoRR, 2023

Ranking Differential Privacy.
CoRR, 2023

2022
Benefit of Interpolation in Nearest Neighbor Algorithms.
SIAM J. Math. Data Sci., June, 2022

Variance reduction on general adaptive stochastic mirror descent.
Mach. Learn., 2022

Distributed Bootstrap for Simultaneous Inference Under High Dimensionality.
J. Mach. Learn. Res., 2022

On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation.
CoRR, 2022

Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies.
CoRR, 2022

Rate-Optimal Contextual Online Matching Bandit.
CoRR, 2022

Residual bootstrap exploration for stochastic linear bandit.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Phase Transition from Clean Training to Adversarial Training.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Why Do Artificially Generated Data Help Adversarial Robustness.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Unlabeled Data Help: Minimax Analysis and Adversarial Robustness.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning.
CoRR, 2021

Optimum-statistical collaboration towards efficient black-box optimization.
CoRR, 2021

On the Algorithmic Stability of Adversarial Training.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Adversarially Robust Estimate and Risk Analysis in Linear Regression.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

On the Generalization Properties of Adversarial Training.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Predictive Power of Nearest Neighbors Algorithm under Random Perturbation.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Online Forgetting Process for Linear Regression Models.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Sparse and Low-Rank Tensor Estimation via Cubic Sketchings.
IEEE Trans. Inf. Theory, 2020

Tensor Graphical Model: Non-Convex Optimization and Statistical Inference.
IEEE Trans. Pattern Anal. Mach. Intell., 2020

Variance Reduction on Adaptive Stochastic Mirror Descent.
CoRR, 2020

Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors.
CoRR, 2020

Online Regularization for High-Dimensional Dynamic Pricing Algorithms.
CoRR, 2020

Residual Bootstrap Exploration for Bandit Algorithms.
CoRR, 2020

Directional Pruning of Deep Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Simultaneous Inference for Massive Data: Distributed Bootstrap.
Proceedings of the 37th International Conference on Machine Learning, 2020

Online Batch Decision-Making with High-Dimensional Covariates.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Nonparametric Bayesian Aggregation for Massive Data.
J. Mach. Learn. Res., 2019

A generalization of regularized dual averaging and its dynamics.
CoRR, 2019

Bootstrapping Upper Confidence Bound.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
Statistical Optimality of Interpolated Nearest Neighbor Algorithms.
CoRR, 2018

2017
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models.
J. Mach. Learn. Res., 2017

2016
Analysing the Semantic Change Based on Word Embedding.
Proceedings of the Natural Language Understanding and Intelligent Applications, 2016

2015
Non-convex Statistical Optimization for Sparse Tensor Graphical Model.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
Nearest Neighbor Classifier with Optimal Stability.
CoRR, 2014


  Loading...