Di Wang

ORCID

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 109

Top Venues

Publications

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 (2024)
Lijie Hu, Tianhao Huang, Huanyi Xie, Chenyang Ren, Zhengyu Hu, Lu Yu, Di Wang
Semi-supervised Concept Bottleneck Models. CoRR (2024)
Liangyu Wang, Junxiao Wang, Di Wang
WiP: Towards Light Adaptation of Large Language Models For Personal Hardware. EdgeFM@MobiSys (2024)
Shaopeng Fu, Di Wang
Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach. ICLR (2024)
Youming Tao, Cheng-Long Wang, Miao Pan, Dongxiao Yu, Xiuzhen Cheng, Di Wang
Communication Efficient and Provable Federated Unlearning. Proc. VLDB Endow. 17 (5) (2024)
Lijie Hu, Ivan Habernal, Lei Shen, Di Wang
Differentially Private Natural Language Models: Recent Advances and Future Directions. EACL (Findings) (2024)
Zihao Luo, Xilie Xu, Feng Liu, Yun Sing Koh, Di Wang, Jingfeng Zhang
Privacy-Preserving Low-Rank Adaptation for Latent Diffusion Models. CoRR (2024)
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 (2024)
Lijie Hu, Chenyang Ren, Zhengyu Hu, Cheng-Long Wang, Di Wang
Editable Concept Bottleneck Models. CoRR (2024)
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 (2024)
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. IEEE Netw. Lett. 6 (2) (2024)
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)
Binhao Ma, Tianhang Zheng, Hongsheng Hu, Di Wang, Shuo Wang, Zhongjie Ba, Zhan Qin, Kui Ren
Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning. CoRR (2024)
Minghua Wang, Yan Hu, Ziyun Huang, Di Wang, Jinhui Xu
Persistent Local Homology in Graph Learning. Trans. Mach. Learn. Res. 2024 (2024)
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 (2024)
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 (2024)
Shu Yang, Jiayuan Su, Han Jiang, Mengdi Li, Keyuan Cheng, Muhammad Asif Ali, Lijie Hu, Di Wang
Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs. CoRR (2024)
Youming Tao, Cheng-Long Wang, Miao Pan, Dongxiao Yu, Xiuzhen Cheng, Di Wang
Communication Efficient and Provable Federated Unlearning. CoRR (2024)
Zihang Xiang, Chenglong Wang, Di Wang
How Does Selection Leak Privacy: Revisiting Private Selection and Improved Results for Hyper-parameter Tuning. CoRR (2024)
Jia Li, Lijie Hu, Zhixian He, Jingfeng Zhang, Tianhang Zheng, Di Wang
Text Guided Image Editing with Automatic Concept Locating and Forgetting. CoRR (2024)
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 (2024)
Meng Ding, Kaiyi Ji, Di Wang, Jinhui Xu
Understanding Forgetting in Continual Learning with Linear Regression. CoRR (2024)
Liyang Zhu, Meng Ding, Vaneet Aggarwal, Jinhui Xu, Di Wang
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model. ICLR (2024)
Songning Lai, Lijie Hu, Junxiao Wang, Laure Berti-Équille, Di Wang
Faithful Vision-Language Interpretation via Concept Bottleneck Models. ICLR (2024)
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 (2024)
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 (2024)
Di Wang, Jinhui Xu
Gradient complexity and non-stationary views of differentially private empirical risk minimization. Theor. Comput. Sci. 982 (2024)
Junren Chen, Michael K. Ng, Di Wang
Quantizing Heavy-Tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery. IEEE Trans. Inf. Theory 70 (3) (2024)
Juexiao Zhou, Longxi Zhou, Di Wang, Xiaopeng Xu, Haoyang Li, Yuetan Chu, Wenkai Han, Xin Gao
Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI. Comput. Biol. Medicine 169 (2024)
Cheng-Long Wang, Mengdi Huai, Di Wang
Inductive Graph Unlearning. CoRR (2023)
Zihang Xiang, Tianhao Wang, Wanyu Lin, Di Wang
Practical Differentially Private and Byzantine-resilient Federated Learning. CoRR (2023)
Cheng-Long Wang, Mengdi Huai, Di Wang
Inductive Graph Unlearning. USENIX Security Symposium (2023)
Jinyan Su, Changhong Zhao, Di Wang
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited. CoRR (2023)
Zihang Xiang, Tianhao Wang, Wanyu Lin, Di Wang
Practical Differentially Private and Byzantine-resilient Federated Learning. Proc. ACM Manag. Data 1 (2) (2023)
Junren Chen, Cheng-Long Wang, Michael K. P. Ng, Di Wang
High Dimensional Statistical Estimation Under Uniformly Dithered One-Bit Quantization. IEEE Trans. Inf. Theory 69 (8) (2023)
Di Wang, Lijie Hu, Huanyu Zhang, Marco Gaboardi, Jinhui Xu
Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data. J. Mach. Learn. Res. 24 (2023)
Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang
SEAT: Stable and Explainable Attention. AAAI (2023)
Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang
Improving Faithfulness for Vision Transformers. CoRR (2023)
Hanshen Xiao, Zihang Xiang, Di Wang, Srinivas Devadas
A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction. SP (2023)
Jinyan Su, Changhong Zhao, Di Wang
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited. UAI (2023)
Jinyan Su, Terry Yue Zhuo, Jonibek Mansurov, Di Wang, Preslav Nakov
Fake News Detectors are Biased against Texts Generated by Large Language Models. CoRR (2023)
Di Wang, Jiahao Ding, Lijie Hu, Zejun Xie, Miao Pan, Jinhui Xu
Finite Sample Guarantees of Differentially Private Expectation Maximization Algorithm. ECAI (2023)
Yulian Wu, Xingyu Zhou, Youming Tao, Di Wang
On Private and Robust Bandits. CoRR (2023)
Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury, Di Wang
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards. CoRR (2023)
Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury, Di Wang
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards. ICML (2023)
Liyang Zhu, Meng Ding, Vaneet Aggarwal, Jinhui Xu, Di Wang
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model. CoRR (2023)
Jinyan Su, Terry Yue Zhuo, Di Wang, Preslav Nakov
DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text. EMNLP (Findings) (2023)
Hanpu Shen, Cheng-Long Wang, Zihang Xiang, Yiming Ying, Di Wang
Differentially Private Non-convex Learning for Multi-layer Neural Networks. CoRR (2023)
Mudit Gaur, Amrit Singh Bedi, Di Wang, Vaneet Aggarwal
On the Global Convergence of Natural Actor-Critic with Two-layer Neural Network Parametrization. CoRR (2023)
Zihang Xiang, Tianhao Wang, Di Wang
Preserving Node-level Privacy in Graph Neural Networks. CoRR (2023)
Bhargav Ganguly, Yulian Wu, Di Wang, Vaneet Aggarwal
Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning. CoRR (2023)
Yulian Wu, Xingyu Zhou, Youming Tao, Di Wang
On Private and Robust Bandits. NeurIPS (2023)
Lijie Hu, Zihang Xiang, Jiabin Liu, Di Wang
Privacy-preserving Sparse Generalized Eigenvalue Problem. AISTATS (2023)
Jinyan Su, Terry Yue Zhuo, Di Wang, Preslav Nakov
DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text. CoRR (2023)
Lijie Hu, Ivan Habernal, Lei Shen, Di Wang
Differentially Private Natural Language Models: Recent Advances and Future Directions. CoRR (2023)
Yulian Wu, Chaowen Guan, Vaneet Aggarwal, Di Wang
Quantum Heavy-tailed Bandits. CoRR (2023)
Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang
SEAT: Stable and Explainable Attention. CoRR (2022)
Lijie Hu, Shuo Ni, Hanshen Xiao, Di Wang
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data. PODS (2022)
Vincent Cohen-Addad, Yunus Esencayi, Chenglin Fan, Marco Gaboardi, Shi Li, Di Wang
On Facility Location Problem in the Local Differential Privacy Model. AISTATS (2022)
Jinyan Su, Lijie Hu, Di Wang
Faster Rates of Private Stochastic Convex Optimization. ALT (2022)
Junren Chen, Michael Kwok-Po Ng, Di Wang
Quantizing Heavy-tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery. CoRR (2022)
Jinyan Su, Jinhui Xu, Di Wang
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data. ACML (2022)
Yuan Qiu, Jinyan Liu, Di Wang
Truthful Generalized Linear Models. WINE (2022)
Di Wang, Jinhui Xu
Inferring ground truth from crowdsourced data under local attribute differential privacy. Theor. Comput. Sci. 865 (2021)
Zhiyu Xue, Shaoyang Yang, Mengdi Huai, Di Wang
Differentially Private Pairwise Learning Revisited. IJCAI (2021)
Di Wang, Jinhui Xu
Differentially private high dimensional sparse covariance matrix estimation. Theor. Comput. Sci. 865 (2021)
Di Wang, Jinhui Xu
On Sparse Linear Regression in the Local Differential Privacy Model. IEEE Trans. Inf. Theory 67 (2) (2021)
Di Wang, Huangyu Zhang, Marco Gaboardi, Jinhui Xu
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data. ALT (2021)
Lijie Hu, Shuo Ni, Hanshen Xiao, Di Wang
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data. CoRR (2021)
Di Wang, Marco Gaboardi, Adam D. Smith, Jinhui Xu
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy. CoRR (2020)
Di Wang, Marco Gaboardi, Adam D. Smith, Jinhui Xu
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy. J. Mach. Learn. Res. 21 (2020)
Di Wang, Jiahao Ding, Zejun Xie, Miao Pan, Jinhui Xu
Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees. CoRR (2020)
Di Wang, Jinhui Xu
Escaping Saddle Points of Empirical Risk Privately and Scalably via DP-Trust Region Method. ECML/PKDD (3) (2020)
Tianhang Zheng, Di Wang, Baochun Li, Jinhui Xu
Towards Assessment of Randomized Mechanisms for Certifying Adversarial Robustness. CoRR (2020)
Di Wang, Hanshen Xiao, Srini Devadas, Jinhui Xu
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data. CoRR (2020)
Mengdi Huai, Chenglin Miao, Jinduo Liu, Di Wang, Jingyuan Chou, Aidong Zhang
Global Interpretation for Patient Similarity Learning. BIBM (2020)
Di Wang, Xiangyu Guo, Shi Li, Jinhui Xu
Robust high dimensional expectation maximization algorithm via trimmed hard thresholding. Mach. Learn. 109 (12) (2020)
Di Wang, Jinhui Xu
Tight lower bound of sparse covariance matrix estimation in the local differential privacy model. Theor. Comput. Sci. 815 (2020)
Di Wang, Xiangyu Guo, Chaowen Guan, Shi Li, Jinhui Xu
Estimating Stochastic Linear Combination of Non-linear Regressions Efficiently and Scalably. CoRR (2020)
Mengdi Huai, Di Wang, Chenglin Miao, Aidong Zhang
Towards Interpretation of Pairwise Learning. AAAI (2020)
Di Wang, Hanshen Xiao, Srinivas Devadas, Jinhui Xu
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data. ICML (2020)
Di Wang, Xiangyu Guo, Chaowen Guan, Shi Li, Jinhui Xu
Estimating stochastic linear combination of non-linear regressions efficiently and scalably. Neurocomputing 399 (2020)
Hongjiang Lei, Di Wang, Ki-Hong Park, Imran Shafique Ansari, Jing Jiang, Gaofeng Pan, Mohamed-Slim Alouini
Safeguarding UAV IoT Communication Systems Against Randomly Located Eavesdroppers. IEEE Internet Things J. 7 (2) (2020)
Di Wang, Xiangyu Guo, Chaowen Guan, Shi Li, Jinhui Xu
Estimating Stochastic Linear Combination of Non-Linear Regressions. AAAI (2020)
Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu, Aidong Zhang
Pairwise Learning with Differential Privacy Guarantees. AAAI (2020)
Di Wang, Xiangyu Guo, Shi Li, Jinhui Xu
Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding. CoRR (2020)
Di Wang, Jinhui Xu
Principal Component Analysis in the local differential privacy model. Theor. Comput. Sci. 809 (2020)
Di Wang, Jinhui Xu
Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation. IJCAI (2019)
Hongjiang Lei, Di Wang, Ki-Hong Park, Imran Shafique Ansari, Gaofeng Pan, Mohamed-Slim Alouini
On Secure UAV Communication Systems with Randomly Located Eavesdroppers. ICCC (2019)
Di Wang, Jinhui Xu
Principal Component Analysis in the Local Differential Privacy Model. IJCAI (2019)
Di Wang, Jinhui Xu
Faster constrained linear regression via two-step preconditioning. Neurocomputing 364 (2019)
Di Wang, Jinhui Xu, Yang He
Estimating Sparse Covariance Matrix Under Differential Privacy via Thresholding. CISS (2019)
Yunus Esencayi, Marco Gaboardi, Shi Li, Di Wang
Facility Location Problem in Differential Privacy Model Revisited. NeurIPS (2019)
Di Wang, Changyou Chen, Jinhui Xu
Differentially Private Empirical Risk Minimization with Non-convex Loss Functions. ICML (2019)
Di Wang, Jinhui Xu
Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View. AAAI (2019)
Di Wang, Adam D. Smith, Jinhui Xu
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations. ALT (2019)
Di Wang, Huanyu Zhang, Marco Gaboardi, Jinhui Xu
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data. CoRR (2019)
Yunus Esencayi, Marco Gaboardi, Shi Li, Di Wang
Facility Location Problem in Differential Privacy Model Revisited. CoRR (2019)
Di Wang, Jinhui Xu
On Sparse Linear Regression in the Local Differential Privacy Model. ICML (2019)
Di Wang, Jinhui Xu
Differentially Private High Dimensional Sparse Covariance Matrix Estimation. CoRR (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Di Wang