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Guang Cheng 0003
Person information
- affiliation: University of California Los Angeles, Department of Statistics, CA, USA
- affiliation: Purdue University, Department of Statistics, West Lafayette, IN, USA
Other persons with the same name
- Guang Cheng — disambiguation page
- Guang Cheng 0001
— Southeast University, Nanjing, China
- Guang Cheng 0002 — University of Florida, Gainesville, FL, USA
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2020 – today
- 2024
- [c21]Yue Xing, Xiaofeng Lin, Qifan Song, Yi Xu, Belinda Zeng, Guang Cheng:
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective. AISTATS 2024: 199-207 - [c20]Joshua John Ward, Xianli Zeng, Guang Cheng:
FairRR: Pre-Processing for Group Fairness through Randomized Response. AISTATS 2024: 3826-3834 - [c19]Yuantong Li, Guang Cheng, Xiaowu Dai:
Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints. ICML 2024 - [i46]Din-Yin Hsieh, Chi-Hua Wang, Guang Cheng:
Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective. CoRR abs/2401.00965 (2024) - [i45]Yinan Cheng, Chi-Hua Wang, Vamsi K. Potluru, Tucker Balch, Guang Cheng:
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models. CoRR abs/2401.00974 (2024) - [i44]Namjoon Suh, Guang Cheng:
A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models. CoRR abs/2401.07187 (2024) - [i43]Yue Xing, Xiaofeng Lin, Qifan Song, Yi Xu, Belinda Zeng, Guang Cheng:
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective. CoRR abs/2401.15248 (2024) - [i42]Yue Xing, Xiaofeng Lin, Namjoon Suh, Qifan Song, Guang Cheng:
Benefits of Transformer: In-Context Learning in Linear Regression Tasks with Unstructured Data. CoRR abs/2402.00743 (2024) - [i41]Shirong Xu, Will Wei Sun, Guang Cheng:
Rate-Optimal Rank Aggregation with Private Pairwise Rankings. CoRR abs/2402.16792 (2024) - [i40]Xianli Zeng, Joshua Ward, Guang Cheng:
FairRR: Pre-Processing for Group Fairness through Randomized Response. CoRR abs/2403.07780 (2024) - [i39]Tian-Yi Zhou, Namjoon Suh, Guang Cheng, Xiaoming Huo:
Approximation of RKHS Functionals by Neural Networks. CoRR abs/2403.12187 (2024) - [i38]Hengzhi He, Peiyu Yu, Junpeng Ren, Ying Nian Wu, Guang Cheng:
Watermarking Generative Tabular Data. CoRR abs/2405.14018 (2024) - [i37]Lan Tao, Shirong Xu, Chi-Hua Wang, Namjoon Suh, Guang Cheng:
Discriminative Estimation of Total Variation Distance: A Fidelity Auditor for Generative Data. CoRR abs/2405.15337 (2024) - [i36]Chi-Hua Wang, Guang Cheng:
BadGD: A unified data-centric framework to identify gradient descent vulnerabilities. CoRR abs/2405.15979 (2024) - [i35]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:
Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space. CoRR abs/2405.16730 (2024) - [i34]Yuantong Li, Guang Cheng, Xiaowu Dai:
Dynamic Online Recommendation for Two-Sided Market with Bayesian Incentive Compatibility. CoRR abs/2406.04374 (2024) - [i33]Joshua Ward, Chi-Hua Wang, Guang Cheng:
Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models. CoRR abs/2406.13012 (2024) - [i32]Yu Xia, Chi-Hua Wang, Joshua Mabry, Guang Cheng:
Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data. CoRR abs/2406.13130 (2024) - [i31]Namjoon Suh, Yuning Yang, Din-Yin Hsieh, Qitong Luan, Shirong Xu, Shixiang Zhu, Guang Cheng:
TimeAutoDiff: Combining Autoencoder and Diffusion model for time series tabular data synthesizing. CoRR abs/2406.16028 (2024) - 2023
- [j9]Wenjie Li, Chi-Hua Wang, Guang Cheng, Qifan Song:
Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization. Trans. Mach. Learn. Res. 2023 (2023) - [j8]Shirong Xu, Chendi Wang, Will Wei Sun, Guang Cheng:
Binary Classification under Local Label Differential Privacy Using Randomized Response Mechanisms. Trans. Mach. Learn. Res. 2023 (2023) - [c18]Ximing Li, Chendi Wang, Guang Cheng:
Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms. ICLR 2023 - [i30]Shirong Xu, Will Wei Sun, Guang Cheng:
Ranking Differential Privacy. CoRR abs/2301.00841 (2023) - [i29]Ximing Li, Chendi Wang, Guang Cheng:
Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms. CoRR abs/2301.08844 (2023) - [i28]Yuantong Li, Guang Cheng, Xiaowu Dai:
Double Matching Under Complementary Preferences. CoRR abs/2301.10230 (2023) - [i27]Shirong Xu, Will Wei Sun, Guang Cheng:
Utility Theory of Synthetic Data Generation. CoRR abs/2305.10015 (2023) - [i26]Namjoon Suh, Xiaofeng Lin, Din-Yin Hsieh, Merhdad Honarkhah, Guang Cheng:
AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing. CoRR abs/2310.15479 (2023) - 2022
- [j7]Yang Yu, Shih-Kang Chao, Guang Cheng:
Distributed Bootstrap for Simultaneous Inference Under High Dimensionality. J. Mach. Learn. Res. 23: 195:1-195:77 (2022) - [j6]Wenjie Li
Variance reduction on general adaptive stochastic mirror descent. Mach. Learn. 111(12): 4639-4677 (2022) - [j5]Yue Xing, Qifan Song, Guang Cheng:
Benefit of Interpolation in Nearest Neighbor Algorithms. SIAM J. Math. Data Sci. 4(2): 935-956 (2022) - [c17]Yue Xing, Qifan Song, Guang Cheng:
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness. AISTATS 2022: 136-168 - [c16]Yue Xing, Qifan Song, Guang Cheng:
Why Do Artificially Generated Data Help Adversarial Robustness. NeurIPS 2022 - [c15]Yue Xing, Qifan Song, Guang Cheng:
Phase Transition from Clean Training to Adversarial Training. NeurIPS 2022 - [c14]Shuang Wu, Chi-Hua Wang, Yuantong Li, Guang Cheng:
Residual bootstrap exploration for stochastic linear bandit. UAI 2022: 2117-2127 - [i25]Yue Xing, Qifan Song, Guang Cheng:
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness. CoRR abs/2202.06996 (2022) - [i24]Shuang Wu, Chi-Hua Wang, Yuantong Li, Guang Cheng:
Residual Bootstrap Exploration for Stochastic Linear Bandit. CoRR abs/2202.11474 (2022) - [i23]Yue Xing, Qifan Song, Guang Cheng:
Benefit of Interpolation in Nearest Neighbor Algorithms. CoRR abs/2202.11817 (2022) - [i22]Yuantong Li
Rate-Optimal Contextual Online Matching Bandit. CoRR abs/2205.03699 (2022) - [i21]Zhanyu Wang
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies. CoRR abs/2210.06140 (2022) - [i20]Yucong Liu, Chi-Hua Wang, Guang Cheng:
On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation. CoRR abs/2211.15809 (2022) - 2021
- [c13]Yuantong Li, Chi-Hua Wang, Guang Cheng:
Online Forgetting Process for Linear Regression Models. AISTATS 2021: 217-225 - [c12]Yue Xing, Qifan Song, Guang Cheng:
Predictive Power of Nearest Neighbors Algorithm under Random Perturbation. AISTATS 2021: 496-504 - [c11]Yue Xing, Qifan Song, Guang Cheng:
On the Generalization Properties of Adversarial Training. AISTATS 2021: 505-513 - [c10]Yue Xing, Ruizhi Zhang, Guang Cheng:
Adversarially Robust Estimate and Risk Analysis in Linear Regression. AISTATS 2021: 514-522 - [c9]Yue Xing, Qifan Song, Guang Cheng:
On the Algorithmic Stability of Adversarial Training. NeurIPS 2021: 26523-26535 - [i19]Wenjie Li, Chi-Hua Wang, Guang Cheng:
Optimum-statistical collaboration towards efficient black-box optimization. CoRR abs/2106.09215 (2021) - [i18]Pratik Ramprasad, Yuantong Li, Zhuoran Yang, Zhaoran Wang, Will Wei Sun, Guang Cheng:
Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning. CoRR abs/2108.03706 (2021) - 2020
- [j4]Xiang Lyu
Tensor Graphical Model: Non-Convex Optimization and Statistical Inference. IEEE Trans. Pattern Anal. Mach. Intell. 42(8): 2024-2037 (2020) - [j3]Botao Hao, Anru Zhang
Sparse and Low-Rank Tensor Estimation via Cubic Sketchings. IEEE Trans. Inf. Theory 66(9): 5927-5964 (2020) - [c8]Botao Hao, Anru R. Zhang, Guang Cheng:
Sparse and Low-rank Tensor Estimation via Cubic Sketchings. AISTATS 2020: 1319-1330 - [c7]Chi-Hua Wang, Guang Cheng:
Online Batch Decision-Making with High-Dimensional Covariates. AISTATS 2020: 3848-3857 - [c6]Yang Yu, Shih-Kang Chao, Guang Cheng:
Simultaneous Inference for Massive Data: Distributed Bootstrap. ICML 2020: 10892-10901 - [c5]Jincheng Bai, Qifan Song, Guang Cheng:
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee. NeurIPS 2020 - [c4]Shih-Kang Chao, Zhanyu Wang, Yue Xing, Guang Cheng:
Directional Pruning of Deep Neural Networks. NeurIPS 2020 - [i17]Yue Xing, Qifan Song, Guang Cheng:
Predictive Power of Nearest Neighbors Algorithm under Random Perturbation. CoRR abs/2002.05304 (2020) - [i16]Chi-Hua Wang, Yang Yu, Botao Hao, Guang Cheng:
Residual Bootstrap Exploration for Bandit Algorithms. CoRR abs/2002.08436 (2020) - [i15]Yang Yu, Shih-Kang Chao, Guang Cheng:
Simultaneous Inference for Massive Data: Distributed Bootstrap. CoRR abs/2002.08443 (2020) - [i14]Chi-Hua Wang, Guang Cheng:
Online Batch Decision-Making with High-Dimensional Covariates. CoRR abs/2002.09438 (2020) - [i13]Shih-Kang Chao, Zhanyu Wang, Yue Xing, Guang Cheng:
Directional Pruning of Deep Neural Networks. CoRR abs/2006.09358 (2020) - [i12]Chi-Hua Wang, Zhanyu Wang, Will Wei Sun, Guang Cheng:
Online Regularization for High-Dimensional Dynamic Pricing Algorithms. CoRR abs/2007.02470 (2020) - [i11]Yue Xing, Qifan Song, Guang Cheng:
On the Generalization Properties of Adversarial Training. CoRR abs/2008.06631 (2020) - [i10]Jincheng Bai, Qifan Song, Guang Cheng:
Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors. CoRR abs/2010.12887 (2020) - [i9]Jincheng Bai, Qifan Song, Guang Cheng:
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee. CoRR abs/2011.07439 (2020) - [i8]Yuantong Li, Chi-Hua Wang, Guang Cheng:
Online Forgetting Process for Linear Regression Models. CoRR abs/2012.01668 (2020) - [i7]Yue Xing, Ruizhi Zhang, Guang Cheng:
Adversarially Robust Estimate and Risk Analysis in Linear Regression. CoRR abs/2012.10278 (2020) - [i6]Wenjie Li, Zhanyu Wang, Yichen Zhang, Guang Cheng:
Variance Reduction on Adaptive Stochastic Mirror Descent. CoRR abs/2012.13760 (2020)
2010 – 2019
- 2019
- [j2]Zuofeng Shang, Botao Hao, Guang Cheng:
Nonparametric Bayesian Aggregation for Massive Data. J. Mach. Learn. Res. 20: 140:1-140:81 (2019) - [c3]Botao Hao, Yasin Abbasi-Yadkori, Zheng Wen, Guang Cheng:
Bootstrapping Upper Confidence Bound. NeurIPS 2019: 12123-12133 - [i5]Botao Hao, Yasin Abbasi-Yadkori, Zheng Wen, Guang Cheng:
Bootstrapping Upper Confidence Bound. CoRR abs/1906.05247 (2019) - [i4]Shih-Kang Chao, Guang Cheng:
A generalization of regularized dual averaging and its dynamics. CoRR abs/1909.10072 (2019) - [i3]Yue Xing
Benefit of Interpolation in Nearest Neighbor Algorithms. CoRR abs/1909.11720 (2019) - 2018
- [i2]Yue Xing
Statistical Optimality of Interpolated Nearest Neighbor Algorithms. CoRR abs/1810.02814 (2018) - 2017
- [j1]Botao Hao, Will Wei Sun, Yufeng Liu, Guang Cheng:
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models. J. Mach. Learn. Res. 18: 217:1-217:58 (2017) - 2016
- [c2]Xuanyi Liao, Guang Cheng:
Analysing the Semantic Change Based on Word Embedding. NLPCC/ICCPOL 2016: 213-223 - 2015
- [c1]Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng:
Non-convex Statistical Optimization for Sparse Tensor Graphical Model. NIPS 2015: 1081-1089 - 2014
- [i1]Wei Sun, Xingye Qiao, Guang Cheng:
Nearest Neighbor Classifier with Optimal Stability. CoRR abs/1405.6642 (2014)
Coauthor Index
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last updated on 2024-12-12 20:55 CET by the dblp team
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