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Di Wang 0015
Person information
- affiliation: King Abdullah University of Science and Technology (KAUST), Saudi Arabia
- affiliation (former): State University of New York at Buffalo, Department of Computer Science and Engineering, USA
Other persons with the same name
- Di Wang — disambiguation page
- Di Wang 0001 — Khalifa University, EBTIC, Abu Dhabi, UAE (and 2 more)
- Di Wang 0002 — Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, Boca Raton, FL, USA
- Di Wang 0003
— Microsoft Research, Redmond, WA, USA (and 1 more)
- Di Wang 0004
- Di Wang 0005
- Di Wang 0006
- Di Wang 0007
- Di Wang 0008
- Di Wang 0009
- Di Wang 0010
- Di Wang 0011
- Di Wang 0012
- Di Wang 0013
- Di Wang 0014
- Di Wang 0016
- Di Wang 0017
- Di Wang 0018
- Di Wang 0019
- Di Wang 0020
- Di Wang 0021
- Di Wang 0022 — Chinese Academy of Agricultural Sciences, Institute of Agricultural Resources and Regional Planning, Beijing, China
- Di Wang 0023
- Di Wang 0024
- Di Wang 0025
- Di Wang 0026
- Di Wang 0027
- Di Wang 0028 — Plus Inc., Suzhou, China (and 1 more)
- Di Wang 0029
- Di Wang 0030 — Microsoft Research, Redmond, WA, USA (and 1 more)
- Di Wang 0031 — Facebook, Seattle, WA, USA (and 1 more)
- Di Wang 0032
- Di Wang 0033
- Di Wang 0034
- Di Wang 0035 — Chongqing Jiaotong University, School of Information Science and Engineering, China (and 2 more)
- Di Wang 0036 — Northeast Forestry University, College of Information and Computer Engineering, Harbin, China (and 1 more)
- Di Wang 0037
- Di Wang 0038
- Di Wang 0039 — Northwest Minzu University, Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Lanzhou, Gansu, China (and 1 more)
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2020 – today
- 2024
- [j23]Juexiao Zhou
Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI. Comput. Biol. Medicine 169: 107861 (2024) - [j22]Yuan Qiu
Truthful and privacy-preserving generalized linear models. Inf. Comput. 301: 105225 (2024) - [j21]Washim Uddin Mondal
Near-Perfect Coverage Manifold Estimation in Cellular Networks via Conditional GAN. IEEE Netw. Lett. 6(2): 97-100 (2024) - [j20]Jinyan Su, Jinhui Xu, Di Wang
PAC learning halfspaces in non-interactive local differential privacy model with public unlabeled data. J. Comput. Syst. Sci. 141: 103496 (2024) - [j19]Jinyan Su, Lijie Hu, Di Wang:
Faster Rates of Differentially Private Stochastic Convex Optimization. J. Mach. Learn. Res. 25: 114:1-114:41 (2024) - [j18]Youming Tao, Cheng-Long Wang
Communication Efficient and Provable Federated Unlearning. Proc. VLDB Endow. 17(5): 1119-1131 (2024) - [j17]Shaowei Wang, Yun Peng, Jin Li, Zikai Wen, Zhipeng Li, Shiyu Yu, Di Wang, Wei Yang:
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened. Proc. VLDB Endow. 17(8): 1870-1883 (2024) - [j16]Di Wang
Gradient complexity and non-stationary views of differentially private empirical risk minimization. Theor. Comput. Sci. 982: 114259 (2024) - [j15]Junren Chen
Quantizing Heavy-Tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery. IEEE Trans. Inf. Theory 70(3): 2003-2038 (2024) - [j14]Minghua Wang, Yan Hu, Ziyun Huang, Di Wang, Jinhui Xu:
Persistent Local Homology in Graph Learning. Trans. Mach. Learn. Res. 2024 (2024) - [c55]Jiahuan Pei, Irene Viola, Haochen Huang, Junxiao Wang, Moonisa Ahsan, Fanghua Ye, Yiming Jiang, Yao Sai, Di Wang, Zhumin Chen, Pengjie Ren, Pablo César:
Autonomous Workflow for Multimodal Fine-Grained Training Assistants Towards Mixed Reality. ACL (Findings) 2024: 4051-4066 - [c54]Lijie Hu, Ivan Habernal, Lei Shen, Di Wang:
Differentially Private Natural Language Models: Recent Advances and Future Directions. EACL (Findings) 2024: 478-499 - [c53]Muhammad Ali, Yan Hu, Jianbin Qin, Di Wang:
Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET). EACL (Findings) 2024: 1462-1473 - [c52]Liangyu Wang
WiP: Towards Light Adaptation of Large Language Models For Personal Hardware. EdgeFM@MobiSys 2024: 30-32 - [c51]Shaopeng Fu, Di Wang:
Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach. ICLR 2024 - [c50]Songning Lai, Lijie Hu, Junxiao Wang, Laure Berti-Équille, Di Wang:
Faithful Vision-Language Interpretation via Concept Bottleneck Models. ICLR 2024 - [c49]Xilie Xu, Keyi Kong, Ning Liu, Lizhen Cui, Di Wang, Jingfeng Zhang, Mohan S. Kankanhalli:
An LLM can Fool Itself: A Prompt-Based Adversarial Attack. ICLR 2024 - [c48]Liyang Zhu, Meng Ding, Vaneet Aggarwal, Jinhui Xu, Di Wang:
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model. ICLR 2024 - [c47]Meng Ding, Kaiyi Ji, Di Wang, Jinhui Xu:
Understanding Forgetting in Continual Learning with Linear Regression. ICML 2024 - [c46]Mudit Gaur, Amrit S. Bedi, Di Wang, Vaneet Aggarwal:
Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization. ICML 2024 - [c45]Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang:
Improving Interpretation Faithfulness for Vision Transformers. ICML 2024 - [c44]Zihang Xiang, Tianhao Wang, Di Wang
Preserving Node-level Privacy in Graph Neural Networks. SP 2024: 4714-4732 - [i69]Muhammad Asif Ali, Yan Hu, Jianbin Qin, Di Wang:
Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET). CoRR abs/2401.10045 (2024) - [i68]Youming Tao, Cheng-Long Wang, Miao Pan, Dongxiao Yu, Xiuzhen Cheng, Di Wang:
Communication Efficient and Provable Federated Unlearning. CoRR abs/2401.11018 (2024) - [i67]Washim Uddin Mondal, Veni Goyal, Satish V. Ukkusuri, Goutam Das, Di Wang, Mohamed-Slim Alouini, Vaneet Aggarwal:
Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN. CoRR abs/2402.06901 (2024) - [i66]Shu Yang, Muhammad Asif Ali, Cheng-Long Wang, Lijie Hu, Di Wang:
MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning. CoRR abs/2402.11260 (2024) - [i65]Shu Yang, Lijie Hu, Lu Yu, Muhammad Asif Ali, Di Wang:
Human-AI Interactions in the Communication Era: Autophagy Makes Large Models Achieving Local Optima. CoRR abs/2402.11271 (2024) - [i64]Zihao Luo, Xilie Xu, Feng Liu, Yun Sing Koh, Di Wang, Jingfeng Zhang:
Privacy-Preserving Low-Rank Adaptation for Latent Diffusion Models. CoRR abs/2402.11989 (2024) - [i63]Zihang Xiang, Chenglong Wang, Di Wang:
How Does Selection Leak Privacy: Revisiting Private Selection and Improved Results for Hyper-parameter Tuning. CoRR abs/2402.13087 (2024) - [i62]Cheng-Long Wang, Qi Li, Zihang Xiang, Di Wang:
Has Approximate Machine Unlearning been evaluated properly? From Auditing to Side Effects. CoRR abs/2403.12830 (2024) - [i61]Shu Yang, Jiayuan Su, Han Jiang, Mengdi Li
Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs. CoRR abs/2404.00486 (2024) - [i60]Muhammad Asif Ali, Zhengping Li, Shu Yang, Keyuan Cheng, Yang Cao, Tianhao Huang, Lijie Hu, Lu Yu, Di Wang:
PROMPT-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression. CoRR abs/2404.00489 (2024) - [i59]Keyuan Cheng, Gang Lin, Haoyang Fei, Yuxuan Zhai, Lu Yu, Muhammad Asif Ali, Lijie Hu, Di Wang:
Multi-hop Question Answering under Temporal Knowledge Editing. CoRR abs/2404.00492 (2024) - [i58]Mudit Gaur, Amrit Singh Bedi, Di Wang, Vaneet Aggarwal:
Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization. CoRR abs/2405.01843 (2024) - [i57]Jiahuan Pei, Irene Viola, Haochen Huang, Junxiao Wang, Moonisa Ahsan
Autonomous Workflow for Multimodal Fine-Grained Training Assistants Towards Mixed Reality. CoRR abs/2405.13034 (2024) - [i56]Keyuan Cheng, Muhammad Asif Ali, Shu Yang, Gang Lin, Yuxuan Zhai, Haoyang Fei, Ke Xu, Lu Yu, Lijie Hu, Di Wang:
Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top. CoRR abs/2405.15452 (2024) - [i55]Lijie Hu, Chenyang Ren, Zhengyu Hu, Cheng-Long Wang, Di Wang:
Editable Concept Bottleneck Models. CoRR abs/2405.15476 (2024) - [i54]Meng Ding, Kaiyi Ji, Di Wang, Jinhui Xu:
Understanding Forgetting in Continual Learning with Linear Regression. CoRR abs/2405.17583 (2024) - [i53]Jia Li, Lijie Hu, Zhixian He, Jingfeng Zhang, Tianhang Zheng, Di Wang:
Text Guided Image Editing with Automatic Concept Locating and Forgetting. CoRR abs/2405.19708 (2024) - [i52]Lijie Hu, Liang Liu, Shu Yang, Xin Chen, Hongru Xiao, Mengdi Li, Pan Zhou, Muhammad Asif Ali, Di Wang:
A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning. CoRR abs/2406.12255 (2024) - [i51]Shaowei Wang, Changyu Dong, Di Wang, Xiangfu Song:
Beyond Statistical Estimation: Differentially Private Individual Computation in the Shuffle Model. CoRR abs/2406.18145 (2024) - [i50]Lijie Hu, Tianhao Huang, Huanyi Xie, Chenyang Ren, Zhengyu Hu, Lu Yu, Di Wang:
Semi-supervised Concept Bottleneck Models. CoRR abs/2406.18992 (2024) - [i49]Binhao Ma, Tianhang Zheng, Hongsheng Hu
Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning. CoRR abs/2407.05112 (2024) - [i48]Xiaochuan Gou, Ziyue Li, Tian Lan, Junpeng Lin, Zhishuai Li, Bingyu Zhao, Chen Zhang, Di Wang, Xiangliang Zhang:
XTraffic: A Dataset Where Traffic Meets Incidents with Explainability and More. CoRR abs/2407.11477 (2024) - [i47]Shaopeng Fu, Xuexue Sun, Ke Qing, Tianhang Zheng, Di Wang:
Pre-trained Encoder Inference: Revealing Upstream Encoders In Downstream Machine Learning Services. CoRR abs/2408.02814 (2024) - [i46]Muhammad Asif Ali, Nawal Daftardar, Mutayyaba Waheed, Jianbin Qin, Di Wang:
MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language. CoRR abs/2409.12257 (2024) - 2023
- [j13]Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi
Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data. J. Mach. Learn. Res. 24: 132:1-132:57 (2023) - [j12]Zihang Xiang
Practical Differentially Private and Byzantine-resilient Federated Learning. Proc. ACM Manag. Data 1(2): 119:1-119:26 (2023) - [j11]Junren Chen
High Dimensional Statistical Estimation Under Uniformly Dithered One-Bit Quantization. IEEE Trans. Inf. Theory 69(8): 5151-5187 (2023) - [c43]Lijie Hu, Yixin Liu
SEAT: Stable and Explainable Attention. AAAI 2023: 12907-12915 - [c42]Lijie Hu, Zihang Xiang, Jiabin Liu, Di Wang:
Privacy-preserving Sparse Generalized Eigenvalue Problem. AISTATS 2023: 5052-5062 - [c41]Di Wang
Finite Sample Guarantees of Differentially Private Expectation Maximization Algorithm. ECAI 2023: 2435-2442 - [c40]Muhammad Asif Ali, Yan Hu, Jianbin Qin, Di Wang
GRI: Graph-based Relative Isomorphism of Word Embedding Spaces. EMNLP (Findings) 2023: 11304-11313 - [c39]Jinyan Su, Terry Yue Zhuo, Di Wang
DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text. EMNLP (Findings) 2023: 12395-12412 - [c38]Xiaochuan Gou
A Fundamental Model with Stable Interpretability for Traffic Forecasting. GeoPrivacy@SIGSPATIAL 2023: 10-13 - [c37]Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury, Di Wang:
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards. ICML 2023: 37880-37918 - [c36]Rui Chen
EEFL: High-Speed Wireless Communications Inspired Energy Efficient Federated Learning over Mobile Devices. MobiSys 2023: 544-556 - [c35]Yulian Wu, Xingyu Zhou, Youming Tao, Di Wang:
On Private and Robust Bandits. NeurIPS 2023 - [c34]Hanshen Xiao, Zihang Xiang, Di Wang
A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction. SP 2023: 2170-2189 - [c33]Jinyan Su, Changhong Zhao, Di Wang:
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited. UAI 2023: 2026-2035 - [c32]Cheng-Long Wang
Inductive Graph Unlearning. USENIX Security Symposium 2023: 3205-3222 - [c31]Muhammad Asif Ali, Maha Alshmrani, Jianbin Qin, Yan Hu, Di Wang
GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings. ArabicNLP 2023: 181-190 - [i45]Lijie Hu, Ivan Habernal, Lei Shen, Di Wang:
Differentially Private Natural Language Models: Recent Advances and Future Directions. CoRR abs/2301.09112 (2023) - [i44]Yulian Wu, Chaowen Guan, Vaneet Aggarwal, Di Wang:
Quantum Heavy-tailed Bandits. CoRR abs/2301.09680 (2023) - [i43]Yulian Wu, Xingyu Zhou, Youming Tao, Di Wang:
On Private and Robust Bandits. CoRR abs/2302.02526 (2023) - [i42]Bhargav Ganguly, Yulian Wu, Di Wang, Vaneet Aggarwal:
Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning. CoRR abs/2302.08617 (2023) - [i41]Juexiao Zhou
Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI. CoRR abs/2302.11571 (2023) - [i40]Jinyan Su, Changhong Zhao, Di Wang:
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited. CoRR abs/2303.18047 (2023) - [i39]Cheng-Long Wang, Mengdi Huai, Di Wang:
Inductive Graph Unlearning. CoRR abs/2304.03093 (2023) - [i38]Zihang Xiang, Tianhao Wang, Wanyu Lin, Di Wang:
Practical Differentially Private and Byzantine-resilient Federated Learning. CoRR abs/2304.09762 (2023) - [i37]Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying, Ding-Xuan Zhou:
Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks. CoRR abs/2305.16891 (2023) - [i36]Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury, Di Wang:
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards. CoRR abs/2306.01121 (2023) - [i35]Jinyan Su, Terry Yue Zhuo, Di Wang, Preslav Nakov:
DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text. CoRR abs/2306.05540 (2023) - [i34]Mudit Gaur, Amrit Singh Bedi, Di Wang, Vaneet Aggarwal:
On the Global Convergence of Natural Actor-Critic with Two-layer Neural Network Parametrization. CoRR abs/2306.10486 (2023) - [i33]Jinyan Su, Terry Yue Zhuo, Jonibek Mansurov, Di Wang, Preslav Nakov:
Fake News Detectors are Biased against Texts Generated by Large Language Models. CoRR abs/2309.08674 (2023) - [i32]Shaopeng Fu, Di Wang:
Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach. CoRR abs/2310.06112 (2023) - [i31]Liyang Zhu, Meng Ding, Vaneet Aggarwal, Jinhui Xu, Di Wang:
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model. CoRR abs/2310.07367 (2023) - [i30]Hanpu Shen, Cheng-Long Wang, Zihang Xiang, Yiming Ying, Di Wang:
Differentially Private Non-convex Learning for Multi-layer Neural Networks. CoRR abs/2310.08425 (2023) - [i29]Muhammad Asif Ali, Yan Hu, Jianbin Qin, Di Wang:
GRI: Graph-based Relative Isomorphism of Word Embedding Spaces. CoRR abs/2310.12360 (2023) - [i28]Muhammad Asif Ali, Maha Alshmrani, Jianbin Qin, Yan Hu, Di Wang:
GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings. CoRR abs/2310.13068 (2023) - [i27]Xilie Xu, Keyi Kong, Ning Liu, Lizhen Cui, Di Wang, Jingfeng Zhang, Mohan S. Kankanhalli
An LLM can Fool Itself: A Prompt-Based Adversarial Attack. CoRR abs/2310.13345 (2023) - [i26]Zihang Xiang, Tianhao Wang, Di Wang:
Preserving Node-level Privacy in Graph Neural Networks. CoRR abs/2311.06888 (2023) - [i25]Jia Li, Lijie Hu, Jingfeng Zhang, Tianhang Zheng, Hua Zhang, Di Wang:
Fair Text-to-Image Diffusion via Fair Mapping. CoRR abs/2311.17695 (2023) - [i24]Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang:
Improving Faithfulness for Vision Transformers. CoRR abs/2311.17983 (2023) - 2022
- [c30]Jinyan Su, Jinhui Xu, Di Wang:
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data. ACML 2022: 927-941 - [c29]Youming Tao, Yulian Wu, Peng Zhao, Di Wang:
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits. AISTATS 2022: 1546-1574 - [c28]Vincent Cohen-Addad, Yunus Esencayi, Chenglin Fan, Marco Gaboardi
On Facility Location Problem in the Local Differential Privacy Model. AISTATS 2022: 3914-3929 - [c27]Jinyan Su, Lijie Hu, Di Wang:
Faster Rates of Private Stochastic Convex Optimization. ALT 2022: 995-1002 - [c26]Youming Tao, Yulian Wu, Xiuzhen Cheng, Di Wang:
Private Stochastic Convex Optimization and Sparse Learning with Heavy-tailed Data Revisited. IJCAI 2022: 3947-3953 - [c25]Di Wang, Jinhui Xu:
Differentially Private ℓ1-norm Linear Regression with Heavy-tailed Data. ISIT 2022: 1856-1861 - [c24]Lijie Hu
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data. PODS 2022: 227-236 - [c23]Yuan Qiu, Jinyan Liu, Di Wang:
Truthful Generalized Linear Models. WINE 2022: 369-370 - [i23]Di Wang
Differentially Private 𝓁1-norm Linear Regression with Heavy-tailed Data. CoRR abs/2201.03204 (2022) - [i22]Junren Chen, Cheng-Long Wang, Michael K. Ng, Di Wang
High Dimensional Statistical Estimation under One-bit Quantization. CoRR abs/2202.13157 (2022) - [i21]Yuan Qiu, Jinyan Liu, Di Wang:
Truthful Generalized Linear Models. CoRR abs/2209.07815 (2022) - [i20]Jinyan Su, Jinhui Xu, Di Wang:
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data. CoRR abs/2209.08319 (2022) - [i19]Hao Wang, Wanyu Lin, Hao He, Di Wang, Chengzhi Mao, Muhan Zhang:
1st ICLR International Workshop on Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (PAIR^2Struct). CoRR abs/2210.03612 (2022) - [i18]Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun
SEAT: Stable and Explainable Attention. CoRR abs/2211.13290 (2022) - [i17]Junren Chen, Michael Kwok-Po Ng, Di Wang:
Quantizing Heavy-tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery. CoRR abs/2212.14562 (2022) - 2021
- [j10]Di Wang
Inferring ground truth from crowdsourced data under local attribute differential privacy. Theor. Comput. Sci. 865: 85-98 (2021) - [j9]Di Wang
Differentially private high dimensional sparse covariance matrix estimation. Theor. Comput. Sci. 865: 119-130 (2021) - [j8]Di Wang
On Sparse Linear Regression in the Local Differential Privacy Model. IEEE Trans. Inf. Theory 67(2): 1182-1200 (2021) - [c22]Di Wang, Huangyu Zhang, Marco Gaboardi
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data. ALT 2021: 1207-1213 - [c21]Zhiyu Xue, Shaoyang Yang, Mengdi Huai, Di Wang:
Differentially Private Pairwise Learning Revisited. IJCAI 2021: 3242-3248 - [i16]Youming Tao
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits. CoRR abs/2106.02575 (2021) - [i15]Lijie Hu, Shuo Ni, Hanshen Xiao, Di Wang
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data. CoRR abs/2107.11136 (2021) - [i14]Jinyan Su, Di Wang:
Faster Rates of Differentially Private Stochastic Convex Optimization. CoRR abs/2108.00331 (2021) - 2020
- [j7]Di Wang
Estimating stochastic linear combination of non-linear regressions efficiently and scalably. Neurocomputing 399: 129-140 (2020) - [j6]Hongjiang Lei
Safeguarding UAV IoT Communication Systems Against Randomly Located Eavesdroppers. IEEE Internet Things J. 7(2): 1230-1244 (2020) - [j5]Di Wang, Marco Gaboardi
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy. J. Mach. Learn. Res. 21: 200:1-200:39 (2020) - [j4]Di Wang
Robust high dimensional expectation maximization algorithm via trimmed hard thresholding. Mach. Learn. 109(12): 2283-2311 (2020) - [j3]Di Wang, Jinhui Xu:
Principal Component Analysis in the local differential privacy model. Theor. Comput. Sci. 809: 296-312 (2020) - [j2]Di Wang, Jinhui Xu:
Tight lower bound of sparse covariance matrix estimation in the local differential privacy model. Theor. Comput. Sci. 815: 47-59 (2020) - [c20]Mengdi Huai, Di Wang, Chenglin Miao
Pairwise Learning with Differential Privacy Guarantees. AAAI 2020: 694-701 - [c19]Mengdi Huai, Di Wang, Chenglin Miao
Towards Interpretation of Pairwise Learning. AAAI 2020: 4166-4173 - [c18]Di Wang, Xiangyu Guo, Chaowen Guan, Shi Li, Jinhui Xu:
Estimating Stochastic Linear Combination of Non-Linear Regressions. AAAI 2020: 6137-6144 - [c17]Mengdi Huai, Chenglin Miao, Jinduo Liu, Di Wang, Jingyuan Chou, Aidong Zhang:
Global Interpretation for Patient Similarity Learning. BIBM 2020: 589-594 - [c16]Di Wang, Hanshen Xiao, Srinivas Devadas, Jinhui Xu:
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data. ICML 2020: 10081-10091 - [c15]Di Wang
Escaping Saddle Points of Empirical Risk Privately and Scalably via DP-Trust Region Method. ECML/PKDD (3) 2020: 90-106 - [i13]Tianhang Zheng, Di Wang, Baochun Li, Jinhui Xu:
Towards Assessment of Randomized Mechanisms for Certifying Adversarial Robustness. CoRR abs/2005.07347 (2020) - [i12]Di Wang, Xiangyu Guo, Chaowen Guan, Shi Li, Jinhui Xu:
Estimating Stochastic Linear Combination of Non-linear Regressions Efficiently and Scalably. CoRR abs/2010.09265 (2020) - [i11]Di Wang, Xiangyu Guo, Shi Li, Jinhui Xu:
Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding. CoRR abs/2010.09576 (2020) - [i10]Di Wang, Hanshen Xiao, Srini Devadas, Jinhui Xu:
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data. CoRR abs/2010.11082 (2020) - [i9]Di Wang, Jiahao Ding, Zejun Xie, Miao Pan, Jinhui Xu:
Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees. CoRR abs/2010.13520 (2020) - [i8]Di Wang, Marco Gaboardi, Adam D. Smith, Jinhui Xu:
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy. CoRR abs/2011.05934 (2020)
2010 – 2019
- 2019
- [j1]Di Wang
Faster constrained linear regression via two-step preconditioning. Neurocomputing 364: 280-296 (2019) - [c14]Di Wang, Jinhui Xu:
Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View. AAAI 2019: 1182-1189 - [c13]Di Wang, Adam D. Smith, Jinhui Xu:
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations. ALT 2019: 897-902 - [c12]Di Wang, Jinhui Xu, Yang He:
Estimating Sparse Covariance Matrix Under Differential Privacy via Thresholding. CISS 2019: 1-5 - [c11]Hongjiang Lei, Di Wang
On Secure UAV Communication Systems with Randomly Located Eavesdroppers. ICCC 2019: 201-206 - [c10]Di Wang, Changyou Chen, Jinhui Xu:
Differentially Private Empirical Risk Minimization with Non-convex Loss Functions. ICML 2019: 6526-6535 - [c9]Di Wang, Jinhui Xu:
On Sparse Linear Regression in the Local Differential Privacy Model. ICML 2019: 6628-6637 - [c8]Mengdi Huai, Di Wang, Chenglin Miao
Privacy-aware Synthesizing for Crowdsourced Data. IJCAI 2019: 2542-2548 - [c7]Di Wang, Jinhui Xu:
Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation. IJCAI 2019: 4788-4794 - [c6]Di Wang, Jinhui Xu:
Principal Component Analysis in the Local Differential Privacy Model. IJCAI 2019: 4795-4801 - [c5]Yunus Esencayi, Marco Gaboardi
Facility Location Problem in Differential Privacy Model Revisited. NeurIPS 2019: 8489-8498 - [i7]Di Wang
Differentially Private High Dimensional Sparse Covariance Matrix Estimation. CoRR abs/1901.06413 (2019) - [i6]Di Wang, Huanyu Zhang, Marco Gaboardi, Jinhui Xu:
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data. CoRR abs/1910.00482 (2019) - [i5]Yunus Esencayi, Marco Gaboardi, Shi Li, Di Wang:
Facility Location Problem in Differential Privacy Model Revisited. CoRR abs/1910.12050 (2019) - 2018
- [c4]Di Wang, Jinhui Xu:
Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning. AAAI 2018: 1439-1446 - [c3]Di Wang, Mengdi Huai, Jinhui Xu:
Differentially Private Sparse Inverse Covariance Estimation. GlobalSIP 2018: 1139-1143 - [c2]Di Wang, Marco Gaboardi
Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited. NeurIPS 2018: 973-982 - [i4]Di Wang, Jinhui Xu:
Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning. CoRR abs/1802.03337 (2018) - [i3]Di Wang, Marco Gaboardi, Jinhui Xu:
Efficient Empirical Risk Minimization with Smooth Loss Functions in Non-interactive Local Differential Privacy. CoRR abs/1802.04085 (2018) - [i2]Di Wang, Minwei Ye, Jinhui Xu:
Differentially Private Empirical Risk Minimization Revisited: Faster and More General. CoRR abs/1802.05251 (2018) - [i1]Di Wang, Adam D. Smith, Jinhui Xu:
Differentially Private Empirical Risk Minimization in Non-interactive Local Model via Polynomial of Inner Product Approximation. CoRR abs/1812.06825 (2018) - 2017
- [c1]Di Wang, Minwei Ye, Jinhui Xu:
Differentially Private Empirical Risk Minimization Revisited: Faster and More General. NIPS 2017: 2722-2731
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