Di Wang

According to our database, Di Wang authored at least 122 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

TAAD: Time-varying adversarial anomaly detection in dynamic graphs.

Guanghua Liu, Jia Zhang, Peng Lv, Chenlong Wang, Huan Wang, Di Wang

Inf. Process. Manag., 2025

2024

Private Over-the-Air Federated Learning at Band-Limited Edge.

Youming Tao, Shuzhen Chen, Congwei Zhang, Di Wang, Dongxiao Yu, Xiuzhen Cheng, Falko Dressler

IEEE Trans. Mob. Comput., December, 2024

Near-Perfect Coverage Manifold Estimation in Cellular Networks via Conditional GAN.

Washim Uddin Mondal, Veni Goyal, Satish V. Ukkusuri, Goutam Das, Di Wang, Mohamed-Slim Alouini, Vaneet Aggarwal

IEEE Netw. Lett., June, 2024

PAC learning halfspaces in non-interactive local differential privacy model with public unlabeled data.

Jinyan Su, Jinhui Xu, Di Wang

J. Comput. Syst. Sci., May, 2024

Privacy Amplification via Shuffling: Unified, Simplified, and Tightened.

Shaowei Wang, Yun Peng, Jin Li, Zikai Wen, Zhipeng Li, Shiyu Yu, Di Wang, Wei Yang

Proc. VLDB Endow., April, 2024

Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI.

Juexiao Zhou, Longxi Zhou, Di Wang, Xiaopeng Xu, Haoyang Li, Yuetan Chu, Wenkai Han, Xin Gao

Comput. Biol. Medicine, February, 2024

Gradient complexity and non-stationary views of differentially private empirical risk minimization.

Di Wang, Jinhui Xu

Theor. Comput. Sci., January, 2024

Communication Efficient and Provable Federated Unlearning.

Youming Tao, Cheng-Long Wang, Miao Pan, Dongxiao Yu, Xiuzhen Cheng, Di Wang

Proc. VLDB Endow., January, 2024

Persistent Local Homology in Graph Learning.

Minghua Wang, Yan Hu, Ziyun Huang, Di Wang, Jinhui Xu

Trans. Mach. Learn. Res., 2024

Quantizing Heavy-Tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery.

Junren Chen, Michael K. Ng, Di Wang

IEEE Trans. Inf. Theory, 2024

Faster Rates of Differentially Private Stochastic Convex Optimization.

Jinyan Su, Lijie Hu, Di Wang

J. Mach. Learn. Res., 2024

Truthful and privacy-preserving generalized linear models.

Yuan Qiu, Jinyan Liu, Di Wang

Inf. Comput., 2024

Towards Multi-dimensional Explanation Alignment for Medical Classification.

Lijie Hu, Songning Lai, Wenshuo Chen, Hongru Xiao, Hongbin Lin, Lu Yu, Jingfeng Zhang, Di Wang

CoRR, 2024

Truthful High Dimensional Sparse Linear Regression.

Liyang Zhu, Amina Manseur, Meng Ding, Jinyan Liu, Jinhui Xu, Di Wang

CoRR, 2024

What makes your model a low-empathy or warmth person: Exploring the Origins of Personality in LLMs.

Shu Yang, Shenzhe Zhu, Ruoxuan Bao, Liang Liu, Yu Cheng, Lijie Hu, Mengdi Li, Di Wang

CoRR, 2024

Faithful Interpretation for Graph Neural Networks.

Lijie Hu, Tianhao Huang, Lu Yu, Wanyu Lin, Tianhang Zheng, Di Wang

CoRR, 2024

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing.

Zhuoran Zhang, Yongxiang Li, Zijian Kan, Keyuan Cheng, Lijie Hu, Di Wang

CoRR, 2024

Understanding Reasoning in Chain-of-Thought from the Hopfieldian View.

Lijie Hu, Liang Liu, Shu Yang, Xin Chen, Zhen Tan, Muhammad Asif Ali, Mengdi Li, Di Wang

CoRR, 2024

MQA-KEAL: Multi-hop Question Answering under Knowledge Editing for Arabic Language.

Muhammad Asif Ali, Nawal Daftardar, Mutayyaba Waheed, Jianbin Qin, Di Wang

CoRR, 2024

Pre-trained Encoder Inference: Revealing Upstream Encoders In Downstream Machine Learning Services.

Shaopeng Fu, Xuexue Sun, Ke Qing, Tianhang Zheng, Di Wang

CoRR, 2024

XTraffic: A Dataset Where Traffic Meets Incidents with Explainability and More.

Xiaochuan Gou, Ziyue Li, Tian Lan, Junpeng Lin, Zhishuai Li, Bingyu Zhao, Chen Zhang, Di Wang, Xiangliang Zhang

CoRR, 2024

Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning.

Binhao Ma, Tianhang Zheng, Hongsheng Hu, Di Wang, Shuo Wang, Zhongjie Ba, Zhan Qin, Kui Ren

CoRR, 2024

Semi-supervised Concept Bottleneck Models.

Lijie Hu, Tianhao Huang, Huanyi Xie, Chenyang Ren, Zhengyu Hu, Lu Yu, Di Wang

CoRR, 2024

Beyond Statistical Estimation: Differentially Private Individual Computation in the Shuffle Model.

Shaowei Wang, Changyu Dong, Di Wang, Xiangfu Song

CoRR, 2024

A Hopfieldian View-based Interpretation for Chain-of-Thought Reasoning.

Lijie Hu, Liang Liu, Shu Yang, Xin Chen, Hongru Xiao, Mengdi Li, Pan Zhou, Muhammad Asif Ali, Di Wang

CoRR, 2024

Text Guided Image Editing with Automatic Concept Locating and Forgetting.

Jia Li, Lijie Hu, Zhixian He, Jingfeng Zhang, Tianhang Zheng, Di Wang

CoRR, 2024

Editable Concept Bottleneck Models.

Lijie Hu, Chenyang Ren, Zhengyu Hu, Cheng-Long Wang, Di Wang

CoRR, 2024

Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top.

Keyuan Cheng, Muhammad Asif Ali, Shu Yang, Gang Lin, Yuxuan Zhai, Haoyang Fei, Ke Xu, Lu Yu, Lijie Hu, Di Wang

CoRR, 2024

Multi-hop Question Answering under Temporal Knowledge Editing.

Keyuan Cheng, Gang Lin, Haoyang Fei, Yuxuan Zhai, Lu Yu, Muhammad Asif Ali, Lijie Hu, Di Wang

CoRR, 2024

PROMPT-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression.

Muhammad Asif Ali, Zhengping Li, Shu Yang, Keyuan Cheng, Yang Cao, Tianhao Huang, Lijie Hu, Lu Yu, Di Wang

CoRR, 2024

Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs.

Shu Yang, Jiayuan Su, Han Jiang, Mengdi Li, Keyuan Cheng, Muhammad Asif Ali, Lijie Hu, Di Wang

CoRR, 2024

Has Approximate Machine Unlearning been evaluated properly? From Auditing to Side Effects.

Cheng-Long Wang, Qi Li, Zihang Xiang, Di Wang

CoRR, 2024

How Does Selection Leak Privacy: Revisiting Private Selection and Improved Results for Hyper-parameter Tuning.

Zihang Xiang, Chenglong Wang, Di Wang

CoRR, 2024

Privacy-Preserving Low-Rank Adaptation for Latent Diffusion Models.

Zihao Luo, Xilie Xu, Feng Liu, Yun Sing Koh, Di Wang, Jingfeng Zhang

CoRR, 2024

Human-AI Interactions in the Communication Era: Autophagy Makes Large Models Achieving Local Optima.

Shu Yang, Lijie Hu, Lu Yu, Muhammad Asif Ali, Di Wang

CoRR, 2024

MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning.

Shu Yang, Muhammad Asif Ali, Cheng-Long Wang, Lijie Hu, Di Wang

CoRR, 2024

Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET).

Muhammad Asif Ali, Yan Hu, Jianbin Qin, Di Wang

CoRR, 2024

Preserving Node-level Privacy in Graph Neural Networks.

Zihang Xiang, Tianhao Wang, Di Wang

Proceedings of the IEEE Symposium on Security and Privacy, 2024

Improving Interpretation Faithfulness for Vision Transformers.

Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization.

Mudit Gaur, Amrit S. Bedi, Di Wang, Vaneet Aggarwal

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Understanding Forgetting in Continual Learning with Linear Regression.

Meng Ding, Kaiyi Ji, Di Wang, Jinhui Xu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model.

Liyang Zhu, Meng Ding, Vaneet Aggarwal, Jinhui Xu, Di Wang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

An LLM can Fool Itself: A Prompt-Based Adversarial Attack.

Xilie Xu, Keyi Kong, Ning Liu, Lizhen Cui, Di Wang, Jingfeng Zhang, Mohan S. Kankanhalli

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Faithful Vision-Language Interpretation via Concept Bottleneck Models.

Songning Lai, Lijie Hu, Junxiao Wang, Laure Berti-Équille, Di Wang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach.

Shaopeng Fu, Di Wang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Private Language Models via Truncated Laplacian Mechanism.

Tianhao Huang, Tao Yang, Ivan Habernal, Lijie Hu, Di Wang

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Dissecting Fine-Tuning Unlearning in Large Language Models.

Yihuai Hong, Yuelin Zou, Lijie Hu, Ziqian Zeng, Di Wang, Haiqin Yang

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

WiP: Towards Light Adaptation of Large Language Models For Personal Hardware.

Liangyu Wang, Junxiao Wang, Di Wang

Proceedings of the Workshop on Edge and Mobile Foundation Models, 2024

Differentially Private Natural Language Models: Recent Advances and Future Directions.

Lijie Hu, Ivan Habernal, Lei Shen, Di Wang

Proceedings of the Findings of the Association for Computational Linguistics: EACL 2024, 2024

Antonym vs Synonym Distinction using InterlaCed Encoder NETworks (ICE-NET).

Muhammad Ali, Yan Hu, Jianbin Qin, Di Wang

Proceedings of the Findings of the Association for Computational Linguistics: EACL 2024, 2024

Autonomous Workflow for Multimodal Fine-Grained Training Assistants Towards Mixed Reality.

Jiahuan Pei, Irene Viola, Haochen Huang, Junxiao Wang, Moonisa Ahsan, Fanghua Ye, Yiming Jiang, Yao Sai, Di Wang, Zhumin Chen, Pengjie Ren, Pablo César

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

High Dimensional Statistical Estimation Under Uniformly Dithered One-Bit Quantization.

Junren Chen, Cheng-Long Wang, Michael K. P. Ng, Di Wang

IEEE Trans. Inf. Theory, August, 2023

Practical Differentially Private and Byzantine-resilient Federated Learning.

Zihang Xiang, Tianhao Wang, Wanyu Lin, Di Wang

Proc. ACM Manag. Data, 2023

Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data.

Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, Jinhui Xu

J. Mach. Learn. Res., 2023

Improving Faithfulness for Vision Transformers.

Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang

CoRR, 2023

Fair Text-to-Image Diffusion via Fair Mapping.

Jia Li, Lijie Hu, Jingfeng Zhang, Tianhang Zheng, Hua Zhang, Di Wang

CoRR, 2023

Differentially Private Non-convex Learning for Multi-layer Neural Networks.

Hanpu Shen, Cheng-Long Wang, Zihang Xiang, Yiming Ying, Di Wang

CoRR, 2023

Fake News Detectors are Biased against Texts Generated by Large Language Models.

Jinyan Su, Terry Yue Zhuo, Jonibek Mansurov, Di Wang, Preslav Nakov

CoRR, 2023

On the Global Convergence of Natural Actor-Critic with Two-layer Neural Network Parametrization.

Mudit Gaur, Amrit Singh Bedi, Di Wang, Vaneet Aggarwal

CoRR, 2023

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks.

Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying, Ding-Xuan Zhou

CoRR, 2023

Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning.

Bhargav Ganguly, Yulian Wu, Di Wang, Vaneet Aggarwal

CoRR, 2023

Quantum Heavy-tailed Bandits.

Yulian Wu, Chaowen Guan, Vaneet Aggarwal, Di Wang

CoRR, 2023

GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings.

Muhammad Asif Ali, Maha Alshmrani, Jianbin Qin, Yan Hu, Di Wang

Proceedings of ArabicNLP 2023, Singapore (Hybrid), December 7, 2023, 2023

Inductive Graph Unlearning.

Cheng-Long Wang, Mengdi Huai, Di Wang

Proceedings of the 32nd USENIX Security Symposium, 2023

Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited.

Jinyan Su, Changhong Zhao, Di Wang

Proceedings of the Uncertainty in Artificial Intelligence, 2023

A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction.

Hanshen Xiao, Zihang Xiang, Di Wang, Srinivas Devadas

Proceedings of the 44th IEEE Symposium on Security and Privacy, 2023

On Private and Robust Bandits.

Yulian Wu, Xingyu Zhou, Youming Tao, Di Wang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

EEFL: High-Speed Wireless Communications Inspired Energy Efficient Federated Learning over Mobile Devices.

Rui Chen, Qiyu Wan, Xinyue Zhang, Xiaoqi Qin, Yanzhao Hou, Di Wang, Xin Fu, Miao Pan

Proceedings of the 21st Annual International Conference on Mobile Systems, 2023

Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards.

Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury, Di Wang

Proceedings of the International Conference on Machine Learning, 2023

A Fundamental Model with Stable Interpretability for Traffic Forecasting.

Xiaochuan Gou, Lijie Hu, Di Wang, Xiangliang Zhang

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies, 2023

DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text.

Jinyan Su, Terry Yue Zhuo, Di Wang, Preslav Nakov

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

GRI: Graph-based Relative Isomorphism of Word Embedding Spaces.

Muhammad Asif Ali, Yan Hu, Jianbin Qin, Di Wang

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Finite Sample Guarantees of Differentially Private Expectation Maximization Algorithm.

Di Wang, Jiahao Ding, Lijie Hu, Zejun Xie, Miao Pan, Jinhui Xu

Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023

Privacy-preserving Sparse Generalized Eigenvalue Problem.

Lijie Hu, Zihang Xiang, Jiabin Liu, Di Wang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

SEAT: Stable and Explainable Attention.

Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

1st ICLR International Workshop on Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (PAIR^2Struct).

Hao Wang, Wanyu Lin, Hao He, Di Wang, Chengzhi Mao, Muhan Zhang

CoRR, 2022

High Dimensional Statistical Estimation under One-bit Quantization.

Junren Chen, Cheng-Long Wang, Michael K. Ng, Di Wang

CoRR, 2022

Differentially Private 𝓁₁-norm Linear Regression with Heavy-tailed Data.

Di Wang, Jinhui Xu

CoRR, 2022

Truthful Generalized Linear Models.

Yuan Qiu, Jinyan Liu, Di Wang

Proceedings of the Web and Internet Economics - 18th International Conference, 2022

High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data.

Lijie Hu, Shuo Ni, Hanshen Xiao, Di Wang

Proceedings of the PODS '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

Differentially Private ℓ1-norm Linear Regression with Heavy-tailed Data.

Di Wang, Jinhui Xu

Proceedings of the IEEE International Symposium on Information Theory, 2022

Private Stochastic Convex Optimization and Sparse Learning with Heavy-tailed Data Revisited.

Youming Tao, Yulian Wu, Xiuzhen Cheng, Di Wang

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Faster Rates of Private Stochastic Convex Optimization.

Jinyan Su, Lijie Hu, Di Wang

Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits.

Youming Tao, Yulian Wu, Peng Zhao, Di Wang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

On Facility Location Problem in the Local Differential Privacy Model.

Vincent Cohen-Addad, Yunus Esencayi, Chenglin Fan, Marco Gaboardi, Shi Li, Di Wang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data.

Jinyan Su, Jinhui Xu, Di Wang

Proceedings of the Asian Conference on Machine Learning, 2022

2021

On Sparse Linear Regression in the Local Differential Privacy Model.

Di Wang, Jinhui Xu

IEEE Trans. Inf. Theory, 2021

Differentially private high dimensional sparse covariance matrix estimation.

Di Wang, Jinhui Xu

Theor. Comput. Sci., 2021

Inferring ground truth from crowdsourced data under local attribute differential privacy.

Di Wang, Jinhui Xu

Theor. Comput. Sci., 2021

Faster Rates of Differentially Private Stochastic Convex Optimization.

Jinyan Su, Di Wang

CoRR, 2021

Differentially Private Pairwise Learning Revisited.

Zhiyu Xue, Shaoyang Yang, Mengdi Huai, Di Wang

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data.

Di Wang, Huangyu Zhang, Marco Gaboardi, Jinhui Xu

Proceedings of the Algorithmic Learning Theory, 2021

2020

Tight lower bound of sparse covariance matrix estimation in the local differential privacy model.

Di Wang, Jinhui Xu

Theor. Comput. Sci., 2020

Principal Component Analysis in the local differential privacy model.

Di Wang, Jinhui Xu

Theor. Comput. Sci., 2020

Robust high dimensional expectation maximization algorithm via trimmed hard thresholding.

Di Wang, Xiangyu Guo, Shi Li, Jinhui Xu

Mach. Learn., 2020

Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy.

Di Wang, Marco Gaboardi, Adam D. Smith, Jinhui Xu

J. Mach. Learn. Res., 2020

Safeguarding UAV IoT Communication Systems Against Randomly Located Eavesdroppers.

Hongjiang Lei, Di Wang, Ki-Hong Park, Imran Shafique Ansari, Jing Jiang, Gaofeng Pan, Mohamed-Slim Alouini

IEEE Internet Things J., 2020

Estimating stochastic linear combination of non-linear regressions efficiently and scalably.

Di Wang, Xiangyu Guo, Chaowen Guan, Shi Li, Jinhui Xu

Neurocomputing, 2020

Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees.

Di Wang, Jiahao Ding, Zejun Xie, Miao Pan, Jinhui Xu

CoRR, 2020

Towards Assessment of Randomized Mechanisms for Certifying Adversarial Robustness.

Tianhang Zheng, Di Wang, Baochun Li, Jinhui Xu

CoRR, 2020

Escaping Saddle Points of Empirical Risk Privately and Scalably via DP-Trust Region Method.

Di Wang, Jinhui Xu

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data.

Di Wang, Hanshen Xiao, Srinivas Devadas, Jinhui Xu

Proceedings of the 37th International Conference on Machine Learning, 2020

Global Interpretation for Patient Similarity Learning.

Mengdi Huai, Chenglin Miao, Jinduo Liu, Di Wang, Jingyuan Chou, Aidong Zhang

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020

Estimating Stochastic Linear Combination of Non-Linear Regressions.

Di Wang, Xiangyu Guo, Chaowen Guan, Shi Li, Jinhui Xu

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Towards Interpretation of Pairwise Learning.

Mengdi Huai, Di Wang, Chenglin Miao, Aidong Zhang

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Pairwise Learning with Differential Privacy Guarantees.

Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu, Aidong Zhang

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Faster constrained linear regression via two-step preconditioning.

Di Wang, Jinhui Xu

Neurocomputing, 2019

Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data.

Di Wang, Huanyu Zhang, Marco Gaboardi, Jinhui Xu

CoRR, 2019

Facility Location Problem in Differential Privacy Model Revisited.

Yunus Esencayi, Marco Gaboardi, Shi Li, Di Wang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation.

Di Wang, Jinhui Xu

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Privacy-aware Synthesizing for Crowdsourced Data.

Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu, Aidong Zhang

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Differentially Private Empirical Risk Minimization with Non-convex Loss Functions.

Di Wang, Changyou Chen, Jinhui Xu

Proceedings of the 36th International Conference on Machine Learning, 2019

On Secure UAV Communication Systems with Randomly Located Eavesdroppers.

Hongjiang Lei, Di Wang, Ki-Hong Park, Imran Shafique Ansari, Gaofeng Pan, Mohamed-Slim Alouini

Proceedings of the 2019 IEEE/CIC International Conference on Communications in China, 2019

Estimating Sparse Covariance Matrix Under Differential Privacy via Thresholding.

Di Wang, Jinhui Xu, Yang He

Proceedings of the 53rd Annual Conference on Information Sciences and Systems, 2019

Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations.

Di Wang, Adam D. Smith, Jinhui Xu

Proceedings of the Algorithmic Learning Theory, 2019

Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View.

Di Wang, Jinhui Xu

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Differentially Private Empirical Risk Minimization in Non-interactive Local Model via Polynomial of Inner Product Approximation.

Di Wang, Adam D. Smith, Jinhui Xu

CoRR, 2018

Efficient Empirical Risk Minimization with Smooth Loss Functions in Non-interactive Local Differential Privacy.

Di Wang, Marco Gaboardi, Jinhui Xu

CoRR, 2018

Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited.

Di Wang, Marco Gaboardi, Jinhui Xu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Differentially Private Sparse Inverse Covariance Estimation.

Di Wang, Mengdi Huai, Jinhui Xu

Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing, 2018

Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning.

Di Wang, Jinhui Xu

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Differentially Private Empirical Risk Minimization Revisited: Faster and More General.

Di Wang, Minwei Ye, Jinhui Xu

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017