Xiaojin Zhang

Orcid: 0000-0001-9065-6852

Affiliations:
  • Huazhong University of Science and Technology, Wuhan, China
  • Hong Kong University of Science and Technology, Hong Kong (former)
  • Chinese University of Hong Kong, Hong Kong (former)


According to our database1, Xiaojin Zhang authored at least 23 papers between 2019 and 2024.

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Bibliography

2024
Improved algorithm for permutation testing.
Theor. Comput. Sci., February, 2024

Corrigendum to "Improved Algorithm for Permutation Testing" [Theoretical Computer Science 986 (2024) 114316].
Theor. Comput. Sci., 2024

Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering.
CoRR, 2024

Deciphering the Interplay between Local Differential Privacy, Average Bayesian Privacy, and Maximum Bayesian Privacy.
CoRR, 2024

Reinforcement Learning as a Catalyst for Robust and Fair Federated Learning: Deciphering the Dynamics of Client Contributions.
CoRR, 2024

CauESC: A Causal Aware Model for Emotional Support Conversation.
CoRR, 2024

2023
Trading Off Privacy, Utility, and Efficiency in Federated Learning.
ACM Trans. Intell. Syst. Technol., December, 2023

No Free Lunch Theorem for Security and Utility in Federated Learning.
ACM Trans. Intell. Syst. Technol., February, 2023

K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning.
CoRR, 2023

Privacy in Large Language Models: Attacks, Defenses and Future Directions.
CoRR, 2023

A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning.
CoRR, 2023

Theoretically Principled Federated Learning for Balancing Privacy and Utility.
CoRR, 2023

Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion.
CoRR, 2023

A Game-theoretic Framework for Federated Learning.
CoRR, 2023

Probably Approximately Correct Federated Learning.
CoRR, 2023

Toward the Tradeoffs Between Privacy, Fairness and Utility in Federated Learning.
Proceedings of the Emerging Information Security and Applications, 2023

2022
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning.
CoRR, 2022

2021
Variance-dependent best arm identification.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Adaptive Double-Exploration Tradeoff for Outlier Detection.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Contextual Combinatorial Conservative Bandits.
CoRR, 2019

Near-Optimal Algorithm for Distribution-Free Junta Testing.
CoRR, 2019

Automatic Ensemble Learning for Online Influence Maximization.
CoRR, 2019


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