Di Wang

According to our database, Di Wang authored at least 169 papers between 2017 and 2025.

Collaborative distances:

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

2025

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

Shaopeng Fu, Di Wang

IEEE Trans. Inf. Theory, August, 2025

Multitask Asynchronous Metalearning for Few-Shot Anomalous Node Detection in Dynamic Networks.

Yifan Hong, Chuanqi Shi, Junyang Chen, Huan Wang, Di Wang

IEEE Trans. Comput. Soc. Syst., August, 2025

RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns.

Xin Chen, Junchao Wu, Shu Yang, Runzhe Zhan, Zeyu Wu, Ziyang Luo, Di Wang, Min Yang, Lidia S. Chao, Derek F. Wong

CoRR, August, 2025

MSRS: Adaptive Multi-Subspace Representation Steering for Attribute Alignment in Large Language Models.

Xinyan Jiang, Lin Zhang, Jiayi Zhang, Qingsong Yang, Guimin Hu, Di Wang, Lijie Hu

CoRR, August, 2025

Understanding and Mitigating Political Stance Cross-topic Generalization in Large Language Models.

Jiayi Zhang, Shu Yang, Junchao Wu, Derek F. Wong, Di Wang

CoRR, August, 2025

When Truth Is Overridden: Uncovering the Internal Origins of Sycophancy in Large Language Models.

Keyu Wang, Jin Li, Shu Yang, Zhuoran Zhang, Di Wang

CoRR, August, 2025

EPM: Evolutionary Perception Method for Anomaly Detection in Noisy Dynamic Graphs.

Huan Wang, Junyang Chen, Yirui Wu, Victor C. M. Leung, Di Wang

IEEE Trans. Knowl. Data Eng., July, 2025

Task-Based Flexible Feature Distillation for LLMs.

Khouloud Saadi, Di Wang

CoRR, July, 2025

Infinite Sampling: Efficient and Stable Grouped RL Training for Large Language Models.

Liangyu Wang, Huanyi Xie, Xinhai Wang, Tianjin Huang, Mengdi Li, Di Wang

CoRR, June, 2025

Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs.

Shu Yang, Junchao Wu, Xuansheng Wu, Derek Wong, Ninhao Liu, Di Wang

CoRR, June, 2025

The Compositional Architecture of Regret in Large Language Models.

Xiangxiang Cui, Shu Yang, Tianjin Huang, Wanyu Lin, Lijie Hu, Di Wang

CoRR, June, 2025

Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images.

Liangliang You, Junchi Yao, Shu Yang, Guimin Hu, Lijie Hu, Di Wang

CoRR, June, 2025

Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs.

Wenrui Zhou, Shu Yang, Qingsong Yang, Zikun Guo, Lijie Hu, Di Wang

CoRR, June, 2025

Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment.

Huanyi Xie, Lijie Hu, Lu Yu, Tianhao Huang, Longfei Li, Meng Li, Jun Zhou, Huan Wang, Di Wang

CoRR, June, 2025

Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness.

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

CoRR, June, 2025

CODEMENV: Benchmarking Large Language Models on Code Migration.

Keyuan Cheng, Xudong Shen, Yihao Yang, Tengyue Wang, Yang Cao, Muhammad Asif Ali, Hanbin Wang, Lijie Hu, Di Wang

CoRR, June, 2025

COMPKE: Complex Question Answering under Knowledge Editing.

Keyuan Cheng, Zijian Kan, Zhixian He, Zhuoran Zhang, Muhammad Asif Ali, Ke Xu, Lijie Hu, Di Wang

CoRR, June, 2025

Understanding and Mitigating Cross-lingual Privacy Leakage via Language-specific and Universal Privacy Neurons.

Wenshuo Dong, Qingsong Yang, Shu Yang, Lijie Hu, Meng Ding, Wanyu Lin, Tianhang Zheng, Di Wang

CoRR, June, 2025

Towards Stable and Explainable Attention Mechanisms.

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

IEEE Trans. Knowl. Data Eng., May, 2025

Understanding How Value Neurons Shape the Generation of Specified Values in LLMs.

Yi Su, Jiayi Zhang, Shu Yang, Xinhai Wang, Lijie Hu, Di Wang

CoRR, May, 2025

Is Your LLM-Based Multi-Agent a Reliable Real-World Planner? Exploring Fraud Detection in Travel Planning.

Junchi Yao, Jianhua Xu, Tianyu Xin, Ziyi Wang, Shenzhe Zhu, Shu Yang, Di Wang

CoRR, May, 2025

Second-Order Convergence in Private Stochastic Non-Convex Optimization.

Youming Tao, Zuyuan Zhang, Dongxiao Yu, Xiuzhen Cheng, Falko Dressler, Di Wang

CoRR, May, 2025

Understanding Aha Moments: from External Observations to Internal Mechanisms.

Shu Yang, Junchao Wu, Xin Chen, Yunze Xiao, Xinyi Yang, Derek F. Wong, Di Wang

CoRR, April, 2025

C² ATTACK: Towards Representation Backdoor on CLIP via Concept Confusion.

Lijie Hu, Junchi Liao, Weimin Lyu, Shaopeng Fu, Tianhao Huang, Shu Yang, Guimin Hu, Di Wang

CoRR, March, 2025

3A Multi-Classification Division-Aggregation Framework for Fake News Detection.

Wen Zhang, Haitao Fu, Huan Wang, Zhiguo Gong, Pan Zhou, Di Wang

IEEE Trans. Big Data, February, 2025

Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation.

Tong Li, Shu Yang, Junchao Wu, Jiyao Wei, Lijie Hu, Mengdi Li, Derek F. Wong, Joshua R. Oltmanns, Di Wang

CoRR, February, 2025

Towards User-level Private Reinforcement Learning with Human Feedback.

Jiaming Zhang, Mingxi Lei, Meng Ding, Mengdi Li, Zihang Xiang, Difei Xu, Jinhui Xu, Di Wang

CoRR, February, 2025

EAP-GP: Mitigating Saturation Effect in Gradient-based Automated Circuit Identification.

Lin Zhang, Wenshuo Dong, Zhuoran Zhang, Shu Yang, Lijie Hu, Ninghao Liu, Pan Zhou, Di Wang

CoRR, February, 2025

Privacy Audit as Bits Transmission: (Im)possibilities for Audit by One Run.

Zihang Xiang, Tianhao Wang, Di Wang

CoRR, January, 2025

Evaluating Data Influence in Meta Learning.

Chenyang Ren, Huanyi Xie, Shu Yang, Meng Ding, Lijie Hu, Di Wang

CoRR, January, 2025

Faithful Interpretation for Graph Neural Networks.

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

Trans. Mach. Learn. Res., 2025

Side-Channel Attacks and New Principles in the Shuffle Model of Differential Privacy.

Shaowei Wang, Jin Li, Changyu Dong, Jin Li, Zhili Zhou, Di Wang, Zikai Wen

IEEE Trans. Inf. Forensics Secur., 2025

Private least absolute deviations with heavy-tailed data.

Di Wang, Jinhui Xu

Theor. Comput. Sci., 2025

Generalization Guarantees of Gradient Descent for Shallow Neural Networks.

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

Neural Comput., 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

LUSTER: Link Prediction Utilizing Shared-Latent Space Representation in Multi-Layer Networks.

Ruohan Yang, Muhammad Asif Ali, Huan Wang, Junyang Chen, Di Wang

Proceedings of the ACM on Web Conference 2025, 2025

Nearly Optimal Differentially Private ReLU Regression.

Meng Ding, Mingxi Lei, Shaowei Wang, Tianhang Zheng, Di Wang, Jinhui Xu

Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2025

Mechanistic Unveiling of Transformer Circuits: Self-Influence as a Key to Model Reasoning.

Lin Zhang, Lijie Hu, Di Wang

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2025, Albuquerque, New Mexico, USA, April 29, 2025

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

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

Proceedings of the 31st International Conference on Computational Linguistics, 2025

Understanding the Repeat Curse in Large Language Models from a Feature Perspective.

Junchi Yao, Shu Yang, Jianhua Xu, Lijie Hu, Mengdi Li, Di Wang

Proceedings of the Findings of the Association for Computational Linguistics, 2025

Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements.

Shu Yang, Shenzhe Zhu, Zeyu Wu, Keyu Wang, Junchi Yao, Junchao Wu, Lijie Hu, Mengdi Li, Derek F. Wong, Di Wang

Proceedings of the Findings of the Association for Computational Linguistics, 2025

COMPKE: Complex Question Answering under Knowledge Editing.

Keyuan Cheng, Zijian Kan, Zhuoran Zhang, Muhammad Asif Ali, Lijie Hu, Di Wang

Proceedings of the Findings of the Association for Computational Linguistics, 2025

Improved Rates of Differentially Private Nonconvex-Strongly-Concave Minimax Optimization.

Ruijia Zhang, Mingxi Lei, Meng Ding, Zihang Xiang, Jinhui Xu, Di Wang

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

Privacy-Preserving Low-Rank Adaptation Against Membership Inference Attacks for Latent Diffusion Models.

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

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

Fair Text-to-Image Diffusion via Fair Mapping.

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

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 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

Fair Single Index Model.

Yidong Wang, Meng Ding, Jinhui Xu, Di Wang

ACM Trans. Knowl. Discov. Data, November, 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

Nearly Optimal Rates of Privacy-Preserving Sparse Generalized Eigenvalue Problem.

Lijie Hu, Zihang Xiang, Jiabin Liu, Di Wang

IEEE Trans. Knowl. Data Eng., 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

Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning.

Qi Li, Cheng-Long Wang, Yinzhi Cao, Di Wang

CoRR, 2024

Dissecting Misalignment of Multimodal Large Language Models via Influence Function.

Lijie Hu, Chenyang Ren, Huanyi Xie, Khouloud Saadi, Shu Yang, Jingfeng Zhang, 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

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

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, Cheng-Long 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

Truthful High Dimensional Sparse Linear Regression.

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

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Perplexity-aware Correction for Robust Alignment with Noisy Preferences.

Keyi Kong, Xilie Xu, Di Wang, Jingfeng Zhang, Mohan S. Kankanhalli

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 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

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Revisiting Differentially Private ReLU Regression.

Meng Ding, Mingxi Lei, Liyang Zhu, Shaowei Wang, Di Wang, Jinhui Xu

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 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

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

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

Some Fundamental Machine Learning Problems in the Differential Privacy Model.

Di Wang

PhD thesis, 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