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ORCIDYu-Xiang Wang 0003
王宇翔
Person information
- unicode name: 王宇翔
- affiliation: University of California, Santa Barbara, Department of Computer Science, CA, USA
- affiliation (former): Amazon Web Services, Palo Alto, CA, USA
- affiliation (PhD): Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
- affiliation (former): National University of Singapore, Department of Mechanical Engineering, Singapore
Other persons with the same name
- Yuxiang Wang (aka: Yu-xiang Wang, Yu-Xiang Wang) — disambiguation page
- Yu-Xiang Wang 0002 (aka: Yuxiang Wang 0002) — National Taiwan University, Taipei, Taiwan
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2020 – today
- 2024
[i93]Rachel Redberg, Antti Koskela, Yu-Xiang Wang:
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners. CoRR abs/2401.00583 (2024)- [i92]Antti Koskela, Rachel Redberg, Yu-Xiang Wang:
Privacy Profiles for Private Selection. CoRR abs/2402.06701 (2024) - [i91]Aditya Golatkar, Alessandro Achille, Luca Zancato, Yu-Xiang Wang, Ashwin Swaminathan, Stefano Soatto:
CPR: Retrieval Augmented Generation for Copyright Protection. CoRR abs/2403.18920 (2024) - 2023
- [c84]Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang:
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy. AISTATS 2023: 3977-4005 - [c83]Dheeraj Baby, Yu-Xiang Wang:
Second Order Path Variationals in Non-Stationary Online Learning. AISTATS 2023: 9024-9075 - [c82]Dan Qiao, Yu-Xiang Wang:
Near-Optimal Differentially Private Reinforcement Learning. AISTATS 2023: 9914-9940 - [c81]Jianyu Xu, Dan Qiao, Yu-Xiang Wang:
Doubly Fair Dynamic Pricing. AISTATS 2023: 9941-9975 - [c80]Dan Qiao, Yu-Xiang Wang:
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation. ICLR 2023 - [c79]Ming Yin, Mengdi Wang, Yu-Xiang Wang:
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient. ICLR 2023 - [c78]Kaiqi Zhang, Yu-Xiang Wang:
Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? ICLR 2023 - [c77]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Optimization on Large Model at Small Cost. ICML 2023: 3192-3218 - [c76]Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang:
Non-stationary Reinforcement Learning under General Function Approximation. ICML 2023: 9976-10007 - [c75]Jiachen Li, Edwin Zhang, Ming Yin, Qinxun Bai, Yu-Xiang Wang, William Yang Wang:
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators. ICML 2023: 20485-20528 - [c74]Chong Liu, Yu-Xiang Wang:
Global Optimization with Parametric Function Approximation. ICML 2023: 22113-22136 - [c73]Xuandong Zhao, Yu-Xiang Wang, Lei Li:
Protecting Language Generation Models via Invisible Watermarking. ICML 2023: 42187-42199 - [c72]Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary C. Lipton, Yu-Xiang Wang:
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. NeurIPS 2023 - [c71]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. NeurIPS 2023 - [c70]Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yian Ma:
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation. NeurIPS 2023 - [c69]Rachel Redberg, Antti Koskela, Yu-Xiang Wang:
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners. NeurIPS 2023 - [c68]Jiachen T. Wang, Yuqing Zhu, Yu-Xiang Wang, Ruoxi Jia, Prateek Mittal:
A Privacy-Friendly Approach to Data Valuation. NeurIPS 2023 - [c67]Chong Liu, Ming Yin, Yu-Xiang Wang:
No-Regret Linear Bandits beyond Realizability. UAI 2023: 1294-1303 - [c66]Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang:
Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter. UAI 2023: 2586-2596 - [i90]Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang:
Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy. CoRR abs/2301.00301 (2023) - [i89]Xuandong Zhao, Yu-Xiang Wang, Lei Li:
Protecting Language Generation Models via Invisible Watermarking. CoRR abs/2302.03162 (2023) - [i88]Dan Qiao, Ming Yin, Yu-Xiang Wang:
Logarithmic Switching Cost in Reinforcement Learning beyond Linear MDPs. CoRR abs/2302.12456 (2023) - [i87]Chong Liu, Ming Yin, Yu-Xiang Wang:
No-Regret Linear Bandits beyond Realizability. CoRR abs/2302.13252 (2023) - [i86]Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu:
Improved Differentially Private Regression via Gradient Boosting. CoRR abs/2303.03451 (2023) - [i85]Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen, Sivaraman Balakrishnan, Zachary Chase Lipton, Yu-Xiang Wang:
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms. CoRR abs/2305.19570 (2023) - [i84]Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang:
Non-stationary Reinforcement Learning under General Function Approximation. CoRR abs/2306.00861 (2023) - [i83]Xuandong Zhao, Kexun Zhang, Yu-Xiang Wang, Lei Li:
Generative Autoencoders as Watermark Attackers: Analyses of Vulnerabilities and Threats. CoRR abs/2306.01953 (2023) - [i82]Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang:
"Private Prediction Strikes Back!" Private Kernelized Nearest Neighbors with Individual Renyi Filter. CoRR abs/2306.07381 (2023) - [i81]Sunil Madhow, Dan Xiao, Ming Yin, Yu-Xiang Wang:
Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data. CoRR abs/2306.14063 (2023) - [i80]Xuandong Zhao, Prabhanjan Ananth, Lei Li, Yu-Xiang Wang:
Provable Robust Watermarking for AI-Generated Text. CoRR abs/2306.17439 (2023) - [i79]Kaiqi Zhang, Zixuan Zhang, Minshuo Chen, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang:
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. CoRR abs/2307.01649 (2023) - [i78]Songtao Feng, Ming Yin, Yu-Xiang Wang, Jing Yang, Yingbin Liang:
Model-Free Algorithm with Improved Sample Efficiency for Zero-Sum Markov Games. CoRR abs/2308.08858 (2023) - [i77]Jiachen T. Wang, Yuqing Zhu, Yu-Xiang Wang, Ruoxi Jia, Prateek Mittal:
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation. CoRR abs/2308.15709 (2023) - [i76]Ruixuan Liu, Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Coupling public and private gradient provably helps optimization. CoRR abs/2310.01304 (2023) - [i75]Yingyu Lin, Yian Ma, Yu-Xiang Wang, Rachel Redberg:
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. CoRR abs/2310.14661 (2023) - [i74]Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yi-An Ma:
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation. CoRR abs/2310.18919 (2023) - [i73]Zhiqi Bu, Ruixuan Liu, Yu-Xiang Wang, Sheng Zha, George Karypis:
On the accuracy and efficiency of group-wise clipping in differentially private optimization. CoRR abs/2310.19215 (2023) - [i72]Chuanhao Li, Chong Liu, Yu-Xiang Wang:
Communication-Efficient Federated Non-Linear Bandit Optimization. CoRR abs/2311.01695 (2023) - 2022
- [j13]Chong Liu, Yu-Xiang Wang:
Doubly Robust Crowdsourcing. J. Artif. Intell. Res. 73: 209-229 (2022) - [c65]Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond. AISTATS 2022: 1805-1845 - [c64]Peng Zhao, Yu-Xiang Wang, Zhi-Hua Zhou:
Non-stationary Online Learning with Memory and Non-stochastic Control. AISTATS 2022: 2101-2133 - [c63]Yuqing Zhu, Jinshuo Dong, Yu-Xiang Wang:
Optimal Accounting of Differential Privacy via Characteristic Function. AISTATS 2022: 4782-4817 - [c62]Yuqing Zhu, Yu-Xiang Wang:
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE. AISTATS 2022: 5622-5635 - [c61]Jianyu Xu, Yu-Xiang Wang:
Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise. AISTATS 2022: 9643-9662 - [c60]Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth
, Michael Kearns, Stefano Soatto:
Mixed Differential Privacy in Computer Vision. CVPR 2022: 8366-8376 - [c59]Xuandong Zhao, Lei Li, Yu-Xiang Wang:
Distillation-Resistant Watermarking for Model Protection in NLP. EMNLP (Findings) 2022: 5044-5055 - [c58]Ming Yin, Yaqi Duan, Mengdi Wang, Yu-Xiang Wang:
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism. ICLR 2022 - [c57]Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang:
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost. ICML 2022: 18031-18061 - [c56]Xuandong Zhao, Lei Li, Yu-Xiang Wang:
Provably Confidential Language Modelling. NAACL-HLT 2022: 943-955 - [c55]Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in LQR Control. NeurIPS 2022 - [c54]Zhiliang Tian, Yingxiu Zhao, Ziyue Huang, Yu-Xiang Wang, Nevin L. Zhang, He He:
SeqPATE: Differentially Private Text Generation via Knowledge Distillation. NeurIPS 2022 - [c53]Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang:
Differentially Private Linear Sketches: Efficient Implementations and Applications. NeurIPS 2022 - [c52]Ming Yin, Wenjing Chen, Mengdi Wang, Yu-Xiang Wang:
Offline stochastic shortest path: Learning, evaluation and towards optimality. UAI 2022: 2278-2288 - [i71]Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond. CoRR abs/2201.08905 (2022) - [i70]Jianyu Xu, Yu-Xiang Wang:
Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise. CoRR abs/2201.11341 (2022) - [i69]Dan Qiao, Ming Yin, Ming Min, Yu-Xiang Wang:
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost. CoRR abs/2202.06385 (2022) - [i68]Ming Yin, Yaqi Duan, Mengdi Wang, Yu-Xiang Wang:
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism. CoRR abs/2203.05804 (2022) - [i67]Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto:
Mixed Differential Privacy in Computer Vision. CoRR abs/2203.11481 (2022) - [i66]Yuqing Zhu, Yu-Xiang Wang:
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE. CoRR abs/2203.16100 (2022) - [i65]Simone Bombari, Alessandro Achille, Zijian Wang, Yu-Xiang Wang, Yusheng Xie, Kunwar Yashraj Singh, Srikar Appalaraju, Vijay Mahadevan, Stefano Soatto:
Towards Differential Relational Privacy and its use in Question Answering. CoRR abs/2203.16701 (2022) - [i64]Kaiqi Zhang, Yu-Xiang Wang:
Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? CoRR abs/2204.09664 (2022) - [i63]Xuandong Zhao, Lei Li, Yu-Xiang Wang:
Provably Confidential Language Modelling. CoRR abs/2205.01863 (2022) - [i62]Dheeraj Baby, Yu-Xiang Wang:
Second Order Path Variationals in Non-Stationary Online Learning. CoRR abs/2205.01921 (2022) - [i61]Fuheng Zhao, Dan Qiao, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang:
Differentially Private Linear Sketches: Efficient Implementations and Applications. CoRR abs/2205.09873 (2022) - [i60]Dan Qiao, Yu-Xiang Wang:
Offline Reinforcement Learning with Differential Privacy. CoRR abs/2206.00810 (2022) - [i59]Ming Yin, Wenjing Chen, Mengdi Wang, Yu-Xiang Wang:
Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality. CoRR abs/2206.04921 (2022) - [i58]Kaiqi Zhang, Ming Yin, Yu-Xiang Wang:
Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks. CoRR abs/2206.05916 (2022) - [i57]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger. CoRR abs/2206.07136 (2022) - [i56]Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in LQR Control. CoRR abs/2206.09257 (2022) - [i55]Jianyu Xu, Dan Qiao, Yu-Xiang Wang:
Doubly Fair Dynamic Pricing. CoRR abs/2209.11837 (2022) - [i54]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Bias-Term only Fine-tuning of Foundation Models. CoRR abs/2210.00036 (2022) - [i53]Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis:
Differentially Private Optimization on Large Model at Small Cost. CoRR abs/2210.00038 (2022) - [i52]Dan Qiao, Yu-Xiang Wang:
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation. CoRR abs/2210.00701 (2022) - [i51]Ming Yin, Mengdi Wang, Yu-Xiang Wang:
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient. CoRR abs/2210.00750 (2022) - [i50]Xuandong Zhao, Lei Li, Yu-Xiang Wang:
Distillation-Resistant Watermarking for Model Protection in NLP. CoRR abs/2210.03312 (2022) - [i49]Chong Liu, Yu-Xiang Wang:
Global Optimization with Parametric Function Approximation. CoRR abs/2211.09100 (2022) - [i48]Jiachen Li, Edwin Zhang, Ming Yin, Qinxun Bai, Yu-Xiang Wang, William Yang Wang:
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators. CoRR abs/2211.15956 (2022) - [i47]Dan Qiao, Yu-Xiang Wang:
Near-Optimal Differentially Private Reinforcement Learning. CoRR abs/2212.04680 (2022) - 2021
- [j12]Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang:
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. J. Mach. Learn. Res. 22: 262:1-262:44 (2021) - [c51]Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang:
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. AISTATS 2021: 838-846 - [c50]Ming Yin, Yu Bai, Yu-Xiang Wang:
Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning. AISTATS 2021: 1567-1575 - [c49]Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang:
An Optimal Reduction of TV-Denoising to Adaptive Online Learning. AISTATS 2021: 2899-2907 - [c48]Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in Exp-Concave Online Learning. COLT 2021: 359-409 - [c47]Hojjat Aghakhani, Dongyu Meng, Yu-Xiang Wang, Christopher Kruegel, Giovanni Vigna:
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability. EuroS&P 2021: 159-178 - [c46]Ming Yin, Yu-Xiang Wang:
Towards Instance-Optimal Offline Reinforcement Learning with Pessimism. NeurIPS 2021: 4065-4078 - [c45]Ming Yin, Yu Bai, Yu-Xiang Wang:
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. NeurIPS 2021: 7677-7688 - [c44]Ming Yin, Yu-Xiang Wang:
Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings. NeurIPS 2021: 12890-12903 - [c43]Jianyu Xu, Yu-Xiang Wang:
Logarithmic Regret in Feature-based Dynamic Pricing. NeurIPS 2021: 13898-13910 - [c42]Rachel Redberg, Yu-Xiang Wang:
Privately Publishable Per-instance Privacy. NeurIPS 2021: 17335-17346 - [c41]Xiaoyong Jin, Yu-Xiang Wang, Xifeng Yan:
Inter-Series Attention Model for COVID-19 Forecasting. SDM 2021: 495-503 - [i46]Dheeraj Baby, Xuandong Zhao, Yu-Xiang Wang:
An Optimal Reduction of TV-Denoising to Adaptive Online Learning. CoRR abs/2101.09438 (2021) - [i45]Ming Yin, Yu Bai, Yu-Xiang Wang:
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction. CoRR abs/2102.01748 (2021) - [i44]Peng Zhao, Yu-Xiang Wang, Zhi-Hua Zhou:
Non-stationary Online Learning with Memory and Non-stochastic Control. CoRR abs/2102.03758 (2021) - [i43]Jianyu Xu, Yu-Xiang Wang:
Logarithmic Regret in Feature-based Dynamic Pricing. CoRR abs/2102.10221 (2021) - [i42]Dheeraj Baby, Yu-Xiang Wang:
Optimal Dynamic Regret in Exp-Concave Online Learning. CoRR abs/2104.11824 (2021) - [i41]Ming Yin, Yu-Xiang Wang:
Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings. CoRR abs/2105.06029 (2021) - [i40]Yuqing Zhu, Jinshuo Dong, Yu-Xiang Wang:
Optimal Accounting of Differential Privacy via Characteristic Function. CoRR abs/2106.08567 (2021) - [i39]Ming Yin, Yu-Xiang Wang:
Towards Instance-Optimal Offline Reinforcement Learning with Pessimism. CoRR abs/2110.08695 (2021) - [i38]Rachel Redberg, Yu-Xiang Wang:
Privately Publishable Per-instance Privacy. CoRR abs/2111.02281 (2021) - [i37]Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Addison J. Hu, Ryan J. Tibshirani:
Multivariate Trend Filtering for Lattice Data. CoRR abs/2112.14758 (2021) - 2020
- [j11]Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan:
Subsampled Rényi Differential Privacy and Analytical Moments Accountant. J. Priv. Confidentiality 10(2) (2020) - [j10]Chieh-Chi Kao, Yu-Xiang Wang, Jonathan Waltman, Pradeep Sen:
Patch-Based Image Hallucination for Super Resolution With Detail Reconstruction From Similar Sample Images. IEEE Trans. Multim. 22(5): 1139-1152 (2020) - [c40]Ming Yin, Yu-Xiang Wang:
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning. AISTATS 2020: 3948-3958 - [c39]Yuqing Zhu, Xiang Yu, Manmohan Chandraker, Yu-Xiang Wang:
Private-kNN: Practical Differential Privacy for Computer Vision. CVPR 2020: 11851-11859 - [c38]Chris Decarolis, Mukul Ram, Seyed Esmaeili, Yu-Xiang Wang, Furong Huang:
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm. ICML 2020: 2421-2431 - [c37]Yuqing Zhu, Yu-Xiang Wang:
Improving Sparse Vector Technique with Renyi Differential Privacy. NeurIPS 2020 - [c36]Dheeraj Baby, Yu-Xiang Wang:
Adaptive Online Estimation of Piecewise Polynomial Trends. NeurIPS 2020 - [c35]Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey J. Gordon:
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift. NeurIPS 2020 - [i36]Ming Yin, Yu-Xiang Wang:
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning. CoRR abs/2001.10742 (2020) - [i35]Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey J. Gordon:
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift. CoRR abs/2003.04475 (2020) - [i34]Hojjat Aghakhani, Dongyu Meng, Yu-Xiang Wang, Christopher Kruegel, Giovanni Vigna:
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability. CoRR abs/2005.00191 (2020) - [i33]Ming Yin, Yu Bai, Yu-Xiang Wang:
Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning. CoRR abs/2007.03760 (2020) - [i32]Dheeraj Baby, Yu-Xiang Wang:
Adaptive Online Estimation of Piecewise Polynomial Trends. CoRR abs/2010.00073 (2020) - [i31]Yuqing Zhu, Xiang Yu, Yi-Hsuan Tsai, Francesco Pittaluga, Masoud Faraki, Manmohan Chandraker, Yu-Xiang Wang:
Voting-based Approaches For Differentially Private Federated Learning. CoRR abs/2010.04851 (2020) - [i30]Xiaoyong Jin, Yu-Xiang Wang, Xifeng Yan:
Inter-Series Attention Model for COVID-19 Forecasting. CoRR abs/2010.13006 (2020) - [i29]Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang:
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning. CoRR abs/2011.03186 (2020)
2010 – 2019
- 2019
- [j9]Xi Chen, Yining Wang, Yu-Xiang Wang:
Technical Note - Nonstationary Stochastic Optimization Under Lp, q-Variation Measures. Oper. Res. 67(6): 1752-1765 (2019) - [j8]Yu-Xiang Wang:
Per-instance Differential Privacy. J. Priv. Confidentiality 9(1) (2019) - [j7]Yining Wang, Yu-Xiang Wang, Aarti Singh:
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data. IEEE Trans. Inf. Theory 65(2): 685-706 (2019) - [j6]Yu-Xiang Wang, Huan Xu, Chenlei Leng:
Provable Subspace Clustering: When LRR Meets SSC. IEEE Trans. Inf. Theory 65(9): 5406-5432 (2019) - [c34]Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan:
Subsampled Renyi Differential Privacy and Analytical Moments Accountant. AISTATS 2019: 1226-1235 - [c33]Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani:
A Higher-Order Kolmogorov-Smirnov Test. AISTATS 2019: 2621-2630 - [c32]Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy:
Imitation-Regularized Offline Learning. AISTATS 2019: 2956-2965 - [c31]Yu Bai, Yu-Xiang Wang, Edo Liberty:
ProxQuant: Quantized Neural Networks via Proximal Operators. ICLR (Poster) 2019 - [c30]Yuqing Zhu, Yu-Xiang Wang:
Poission Subsampled Rényi Differential Privacy. ICML 2019: 7634-7642 - [c29]Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, Xifeng Yan:
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. NeurIPS 2019: 5244-5254 - [c28]Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang:
Provably Efficient Q-Learning with Low Switching Cost. NeurIPS 2019: 8002-8011 - [c27]Tengyang Xie, Yifei Ma, Yu-Xiang Wang:
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. NeurIPS 2019: 9665-9675 - [c26]Dheeraj Baby, Yu-Xiang Wang:
Online Forecasting of Total-Variation-bounded Sequences. NeurIPS 2019: 11069-11079 - [i28]Yifei Ma, Yu-Xiang Wang, Balakrishnan Narayanaswamy:
Imitation-Regularized Offline Learning. CoRR abs/1901.04723 (2019) - [i27]Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani:
A Higher-Order Kolmogorov-Smirnov Test. CoRR abs/1903.10083 (2019) - [i26]Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang:
Provably Efficient Q-Learning with Low Switching Cost. CoRR abs/1905.12849 (2019) - [i25]Dheeraj Baby, Yu-Xiang Wang:
Online Forecasting of Total-Variation-bounded Sequences. CoRR abs/1906.03364 (2019) - [i24]Tengyang Xie, Yifei Ma, Yu-Xiang Wang:
Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. CoRR abs/1906.03393 (2019) - [i23]Chong Liu, Yu-Xiang Wang:
Doubly Robust Crowdsourcing. CoRR abs/1906.08591 (2019) - [i22]Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, Xifeng Yan:
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. CoRR abs/1907.00235 (2019) - 2018
- [c25]Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar:
Compression by the signs: distributed learning is a two-way street. ICLR (Workshop) 2018 - [c24]Borja Balle, Yu-Xiang Wang:
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. ICML 2018: 403-412 - [c23]Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar:
SIGNSGD: Compressed Optimisation for Non-Convex Problems. ICML 2018: 559-568 - [c22]Zachary C. Lipton, Yu-Xiang Wang, Alexander J. Smola:
Detecting and Correcting for Label Shift with Black Box Predictors. ICML 2018: 3128-3136 - [c21]Yu-Xiang Wang:
Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain. UAI 2018: 93-103 - [i21]Zachary C. Lipton, Yu-Xiang Wang, Alexander J. Smola:
Detecting and Correcting for Label Shift with Black Box Predictors. CoRR abs/1802.03916 (2018) - [i20]Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar:
signSGD: compressed optimisation for non-convex problems. CoRR abs/1802.04434 (2018) - [i19]Yu-Xiang Wang:
Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain. CoRR abs/1803.02596 (2018) - [i18]Borja Balle, Yu-Xiang Wang:
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising. CoRR abs/1805.06530 (2018) - [i17]Chieh-Chi Kao, Yu-Xiang Wang, Jonathan Waltman, Pradeep Sen:
Patch-Based Image Hallucination for Super Resolution with Detail Reconstruction from Similar Sample Images. CoRR abs/1806.00874 (2018) - [i16]Yu-Xiang Wang, Borja Balle, Shiva Prasad Kasiviswanathan:
Subsampled Rényi Differential Privacy and Analytical Moments Accountant. CoRR abs/1808.00087 (2018) - [i15]Yu Bai, Yu-Xiang Wang, Edo Liberty:
ProxQuant: Quantized Neural Networks via Proximal Operators. CoRR abs/1810.00861 (2018) - 2017
- [b1]Yu-Xiang Wang:
New Paradigms and Optimality Guarantees in Statistical Learning and Estimation. Carnegie Mellon University, USA, 2017 - [c20]Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng:
Attributing Hacks. AISTATS 2017: 794-802 - [c19]Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudík:
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. ICML 2017: 3589-3597 - [c18]Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James Sharpnack, Ryan J. Tibshirani:
Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods. NIPS 2017: 5800-5810 - [i14]Yu-Xiang Wang:
Per-instance Differential Privacy and the Adaptivity of Posterior Sampling in Linear and Ridge regression. CoRR abs/1707.07708 (2017) - [i13]Xi Chen, Yining Wang, Yu-Xiang Wang:
Non-stationary Stochastic Optimization with Local Spatial and Temporal Changes. CoRR abs/1708.03020 (2017) - 2016
- [j5]Yu-Xiang Wang, Huan Xu:
Noisy Sparse Subspace Clustering. J. Mach. Learn. Res. 17: 12:1-12:41 (2016) - [j4]Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. J. Mach. Learn. Res. 17: 105:1-105:41 (2016) - [j3]Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg:
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle. J. Mach. Learn. Res. 17: 183:1-183:40 (2016) - [c17]Yining Wang, Yu-Xiang Wang, Aarti Singh:
Graph Connectivity in Noisy Sparse Subspace Clustering. AISTATS 2016: 538-546 - [c16]Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani:
Graph Sparsification Approaches for Laplacian Smoothing. AISTATS 2016: 1250-1259 - [c15]Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing:
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. ICML 2016: 1548-1557 - [c14]Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Ryan J. Tibshirani:
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers. NIPS 2016: 3513-3521 - [c13]Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg:
On-Average KL-Privacy and Its Equivalence to Generalization for Max-Entropy Mechanisms. PSD 2016: 121-134 - [c12]Mu Li, Ziqi Liu, Alexander J. Smola, Yu-Xiang Wang:
DiFacto: Distributed Factorization Machines. WSDM 2016: 377-386 - [i12]Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg:
A Minimax Theory for Adaptive Data Analysis. CoRR abs/1602.04287 (2016) - [i11]Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg:
On-Average KL-Privacy and its equivalence to Generalization for Max-Entropy Mechanisms. CoRR abs/1605.02277 (2016) - [i10]Yining Wang, Yu-Xiang Wang, Aarti Singh:
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data. CoRR abs/1610.07650 (2016) - [i9]Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng:
Attributing Hacks. CoRR abs/1611.03021 (2016) - [i8]Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudík:
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits. CoRR abs/1612.01205 (2016) - 2015
- [j2]Yu-Xiang Wang, Choon Meng Lee, Loong-Fah Cheong, Kim-Chuan Toh:
Practical Matrix Completion and Corruption Recovery Using Proximal Alternating Robust Subspace Minimization. Int. J. Comput. Vis. 111(3): 315-344 (2015) - [c11]Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. AISTATS 2015 - [c10]Yining Wang, Yu-Xiang Wang, Aarti Singh:
A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data. ICML 2015: 1422-1431 - [c9]Yu-Xiang Wang, Stephen E. Fienberg, Alexander J. Smola:
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo. ICML 2015: 2493-2502 - [c8]Seth R. Flaxman, Yu-Xiang Wang, Alexander J. Smola:
Who Supported Obama in 2012?: Ecological Inference through Distribution Regression. KDD 2015: 289-298 - [c7]Yining Wang, Yu-Xiang Wang, Aarti Singh:
Differentially private subspace clustering. NIPS 2015: 1000-1008 - [c6]Ziqi Liu, Yu-Xiang Wang, Alexander J. Smola:
Fast Differentially Private Matrix Factorization. RecSys 2015: 171-178 - [i7]Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg:
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle. CoRR abs/1502.06309 (2015) - [i6]Yu-Xiang Wang, Stephen E. Fienberg, Alexander J. Smola:
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo. CoRR abs/1502.07645 (2015) - [i5]Yining Wang, Yu-Xiang Wang, Aarti Singh:
Clustering Consistent Sparse Subspace Clustering. CoRR abs/1504.01046 (2015) - [i4]Ziqi Liu, Yu-Xiang Wang, Alexander J. Smola:
Fast Differentially Private Matrix Factorization. CoRR abs/1505.01419 (2015) - 2014
- [j1]Zhi Gao, Loong-Fah Cheong, Yu-Xiang Wang:
Block-Sparse RPCA for Salient Motion Detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(10): 1975-1987 (2014) - [c5]Yu-Xiang Wang, Alexander J. Smola, Ryan J. Tibshirani:
The Falling Factorial Basis and Its Statistical Applications. ICML 2014: 730-738 - [i3]Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. CoRR abs/1410.7690 (2014) - 2013
- [c4]Yu-Xiang Wang, Huan Xu:
Noisy Sparse Subspace Clustering. ICML (1) 2013: 89-97 - [c3]Yu-Xiang Wang, Huan Xu, Chenlei Leng:
Provable Subspace Clustering: When LRR meets SSC. NIPS 2013: 64-72 - [i2]Yu-Xiang Wang, Choon Meng Lee, Loong-Fah Cheong, Kim-Chuan Toh:
Practical Matrix Completion and Corruption Recovery using Proximal Alternating Robust Subspace Minimization. CoRR abs/1309.1539 (2013) - 2012
- [c2]Yu-Xiang Wang, Huan Xu:
Stability of matrix factorization for collaborative filtering. ICML 2012 - [i1]Yu-Xiang Wang, Huan Xu:
Stability of matrix factorization for collaborative filtering. CoRR abs/1206.4640 (2012) - 2011
- [c1]Yu-Xiang Wang, Bin Wang, Li Chen:
Real-Time Volume Caustics with Image-Based Photon Tracing. CAD/Graphics 2011: 71-78
Coauthor Index
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