Di Wang
Orcid: 0000-0003-4908-0243Affiliations:
- King Abdullah University of Science and Technology (KAUST), Saudi Arabia
- State University of New York at Buffalo, Department of Computer Science and Engineering, USA (former)
According to our database1,
Di Wang
authored at least 106 papers
between 2017 and 2024.
Collaborative distances:
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Bibliography
2024
IEEE Netw. Lett., June, 2024
PAC learning halfspaces in non-interactive local differential privacy model with public unlabeled data.
J. Comput. Syst. Sci., May, 2024
Proc. VLDB Endow., April, 2024
Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI.
Comput. Biol. Medicine, February, 2024
Gradient complexity and non-stationary views of differentially private empirical risk minimization.
Theor. Comput. Sci., January, 2024
Proc. VLDB Endow., January, 2024
Quantizing Heavy-Tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery.
IEEE Trans. Inf. Theory, 2024
CoRR, 2024
Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning.
CoRR, 2024
Beyond Statistical Estimation: Differentially Private Individual Computation in the Shuffle Model.
CoRR, 2024
Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization.
CoRR, 2024
CoRR, 2024
CoRR, 2024
Has Approximate Machine Unlearning been evaluated properly? From Auditing to Side Effects.
CoRR, 2024
How Does Selection Leak Privacy: Revisiting Private Selection and Improved Results for Hyper-parameter Tuning.
CoRR, 2024
Human-AI Interactions in the Communication Era: Autophagy Makes Large Models Achieving Local Optima.
CoRR, 2024
CoRR, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Proceedings of the Workshop on Edge and Mobile Foundation Models, 2024
Differentially Private Natural Language Models: Recent Advances and Future Directions.
Proceedings of the Findings of the Association for Computational Linguistics: EACL 2024, 2024
Proceedings of the Findings of the Association for Computational Linguistics: EACL 2024, 2024
2023
High Dimensional Statistical Estimation Under Uniformly Dithered One-Bit Quantization.
IEEE Trans. Inf. Theory, August, 2023
Proc. ACM Manag. Data, 2023
Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data.
J. Mach. Learn. Res., 2023
CoRR, 2023
CoRR, 2023
On the Global Convergence of Natural Actor-Critic with Two-layer Neural Network Parametrization.
CoRR, 2023
CoRR, 2023
Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning.
CoRR, 2023
Proceedings of ArabicNLP 2023, Singapore (Hybrid), December 7, 2023, 2023
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited.
Proceedings of the Uncertainty in Artificial Intelligence, 2023
Proceedings of the 44th IEEE Symposium on Security and Privacy, 2023
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.
Proceedings of the 21st Annual International Conference on Mobile Systems, 2023
Proceedings of the International Conference on Machine Learning, 2023
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.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023
Finite Sample Guarantees of Differentially Private Expectation Maximization Algorithm.
Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023
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).
CoRR, 2022
CoRR, 2022
Proceedings of the Web and Internet Economics - 18th International Conference, 2022
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data.
Proceedings of the PODS '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022
Proceedings of the IEEE International Symposium on Information Theory, 2022
Private Stochastic Convex Optimization and Sparse Learning with Heavy-tailed Data Revisited.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022
Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022
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.
Proceedings of the Asian Conference on Machine Learning, 2022
2021
IEEE Trans. Inf. Theory, 2021
Theor. Comput. Sci., 2021
Inferring ground truth from crowdsourced data under local attribute differential privacy.
Theor. Comput. Sci., 2021
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.
Proceedings of the Algorithmic Learning Theory, 2021
2020
Tight lower bound of sparse covariance matrix estimation in the local differential privacy model.
Theor. Comput. Sci., 2020
Theor. Comput. Sci., 2020
Robust high dimensional expectation maximization algorithm via trimmed hard thresholding.
Mach. Learn., 2020
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy.
J. Mach. Learn. Res., 2020
IEEE Internet Things J., 2020
Estimating stochastic linear combination of non-linear regressions efficiently and scalably.
Neurocomputing, 2020
Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees.
CoRR, 2020
CoRR, 2020
Escaping Saddle Points of Empirical Risk Privately and Scalably via DP-Trust Region Method.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020
Proceedings of the 37th International Conference on Machine Learning, 2020
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020
2019
Neurocomputing, 2019
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data.
CoRR, 2019
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019
Proceedings of the 36th International Conference on Machine Learning, 2019
Proceedings of the 2019 IEEE/CIC International Conference on Communications in China, 2019
Proceedings of the 53rd Annual Conference on Information Sciences and Systems, 2019
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations.
Proceedings of the Algorithmic Learning Theory, 2019
Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View.
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.
CoRR, 2018
Efficient Empirical Risk Minimization with Smooth Loss Functions in Non-interactive Local Differential Privacy.
CoRR, 2018
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing, 2018
Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018
2017
Differentially Private Empirical Risk Minimization Revisited: Faster and More General.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017