Yuanshun Yao

According to our database1, Yuanshun Yao authored at least 36 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2024
Toward Optimal LLM Alignments Using Two-Player Games.
CoRR, 2024

Label Smoothing Improves Machine Unlearning.
CoRR, 2024

Learning to Watermark LLM-generated Text via Reinforcement Learning.
CoRR, 2024

Improving Reinforcement Learning from Human Feedback Using Contrastive Rewards.
CoRR, 2024

Fair Classifiers Without Fair Training: An Influence-Guided Data Sampling Approach.
CoRR, 2024

Measuring and Reducing LLM Hallucination without Gold-Standard Answers via Expertise-Weighting.
CoRR, 2024

Rethinking Machine Unlearning for Large Language Models.
CoRR, 2024

Human-Instruction-Free LLM Self-Alignment with Limited Samples.
CoRR, 2024

Fair Classifiers that Abstain without Harm.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
"My face, my rules": Enabling Personalized Protection Against Unacceptable Face Editing.
Proc. Priv. Enhancing Technol., July, 2023

Large Language Model Unlearning.
CoRR, 2023

Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment.
CoRR, 2023

Understanding Unfairness via Training Concept Influence.
CoRR, 2023

Label Inference Attack against Split Learning under Regression Setting.
CoRR, 2023

Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes.
Proceedings of the International Conference on Machine Learning, 2023

DPAUC: Differentially Private AUC Computation in Federated Learning.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Learning to Counterfactually Explain Recommendations.
CoRR, 2022

Evaluating Fairness Without Sensitive Attributes: A Framework Using Only Auxiliary Models.
CoRR, 2022

Differentially Private AUC Computation in Vertical Federated Learning.
CoRR, 2022

Differentially Private Label Protection in Split Learning.
CoRR, 2022

Label Leakage and Protection from Forward Embedding in Vertical Federated Learning.
CoRR, 2022

Counterfactually Evaluating Explanations in Recommender Systems.
CoRR, 2022

Differentially private multi-party data release for linear regression.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

2021
Defending against Reconstruction Attack in Vertical Federated Learning.
CoRR, 2021

Vertical Federated Learning without Revealing Intersection Membership.
CoRR, 2021

Backdoor Attacks Against Deep Learning Systems in the Physical World.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Backdoor Attacks on Facial Recognition in the Physical World.
CoRR, 2020

2019
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks.
CoRR, 2019

Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks.
Proceedings of the 2019 IEEE Symposium on Security and Privacy, 2019

Latent Backdoor Attacks on Deep Neural Networks.
Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019

2018
With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning.
Proceedings of the 27th USENIX Security Symposium, 2018

2017
Identifying Value in Crowdsourced Wireless Signal Measurements.
Proceedings of the 26th International Conference on World Wide Web, 2017

Object Recognition and Navigation using a Single Networking Device.
Proceedings of the 15th Annual International Conference on Mobile Systems, 2017

Complexity vs. performance: empirical analysis of machine learning as a service.
Proceedings of the 2017 Internet Measurement Conference, 2017

Automated Crowdturfing Attacks and Defenses in Online Review Systems.
Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2017

2016
A general framework to increase the robustness of model-based change point detection algorithms to outliers and noise.
Proceedings of the 2016 SIAM International Conference on Data Mining, 2016


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