Junyuan Hong

Ulisses Braga-Neto

CoRR, 2024

GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning.

[BibT_eX]

[DOI]

CoRR, 2024

Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2024

FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics.

[BibT_eX]

[DOI]

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark.

[BibT_eX]

[DOI]

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

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression.

[BibT_eX]

[DOI]

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

Safe and Robust Watermark Injection with a Single OoD Image.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

On the Generalization Ability of Unsupervised Pretraining.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2023

FedNoisy: Federated Noisy Label Learning Benchmark.

[BibT_eX]

[DOI]

CoRR, 2023

On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space.

[BibT_eX]

[DOI]

CoRR, 2023

Understanding Deep Gradient Leakage via Inversion Influence Functions.

[BibT_eX]

[DOI]

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

International Workshop on Federated Learning for Distributed Data Mining.

[BibT_eX]

[DOI]

Vishnu Naresh Boddeti

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Revisiting Data-Free Knowledge Distillation with Poisoned Teachers.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

MECTA: Memory-Economic Continual Test-Time Model Adaptation.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning.

[BibT_eX]

[DOI]

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

2022

Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Resilient and Communication Efficient Learning for Heterogeneous Federated Systems.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent.

[BibT_eX]

[DOI]

Zhangyang Wang

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

2021

Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2021

On Dynamic Noise Influence in Differentially Private Learning.

[BibT_eX]

[DOI]

Zhangyang Wang

CoRR, 2021

Federated Adversarial Debiasing for Fair and Transferable Representations.

[BibT_eX]

[DOI]

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Data-Free Knowledge Distillation for Heterogeneous Federated Learning.

[BibT_eX]

[DOI]

Zhuangdi Zhu

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Model-Based Privacy Protection under Budget Constraints.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2019

Short Sequence Classification Through Discriminable Linear Dynamical System.

[BibT_eX]

[DOI]

Yang Li

IEEE Trans. Neural Networks Learn. Syst., 2019

Variant Grassmann Manifolds: A Representation Augmentation Method for Action Recognition.

[BibT_eX]

[DOI]

Yang Li

ACM Trans. Knowl. Discov. Data, 2019

2018

Disturbance Grassmann Kernels for Subspace-Based Learning.

[BibT_eX]

[DOI]

Feng Lin

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

2016

Sequential Data Classification in the Space of Liquid State Machines.

[BibT_eX]

[DOI]

Yang Li