Yedi Zhang

Orcid: 0000-0003-1005-2114

According to our database1, Yedi Zhang authored at least 31 papers between 2019 and 2026.

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Bibliography

2026
SafeClaw-R: Towards Safe and Secure Multi-Agent Personal Assistants.
CoRR, March, 2026

LLM-enabled Applications Require System-Level Threat Monitoring.
CoRR, February, 2026

2025
Saddle-to-Saddle Dynamics Explains A Simplicity Bias Across Neural Network Architectures.
CoRR, December, 2025

Towards Stealthy and Effective Backdoor Attacks on Lane Detection: A Naturalistic Data Poisoning Approach.
CoRR, August, 2025

Towards Powerful and Practical Patch Attacks for 2D Object Detection in Autonomous Driving.
CoRR, August, 2025

RvLLM: LLM Runtime Verification with Domain Knowledge.
CoRR, May, 2025

When Are Bias-Free ReLU Networks Effectively Linear Networks?
Trans. Mach. Learn. Res., 2025

Verification of Bit-Flip Attacks against Quantized Neural Networks.
Proc. ACM Program. Lang., 2025

Whispering Under the Eaves: Protecting User Privacy Against Commercial and LLM-powered Automatic Speech Recognition Systems.
Proceedings of the 34th USENIX Security Symposium, 2025

SongBsAb: A Dual Prevention Approach against Singing Voice Conversion based Illegal Song Covers.
Proceedings of the 32nd Annual Network and Distributed System Security Symposium, 2025

Training Dynamics of In-Context Learning in Linear Attention.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

Position: Trustworthy AI Agents Require the Integration of Large Language Models and Formal Methods.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024
The Fusion of Large Language Models and Formal Methods for Trustworthy AI Agents: A Roadmap.
CoRR, 2024

When Are Bias-Free ReLU Networks Like Linear Networks?
CoRR, 2024

A Proactive and Dual Prevention Mechanism against Illegal Song Covers empowered by Singing Voice Conversion.
CoRR, 2024

SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems.
Proceedings of the 31st Annual Network and Distributed System Security Symposium, 2024

Revisiting the Conflict-Resolving Problem from a Semantic Perspective.
Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, 2024

Understanding Unimodal Bias in Multimodal Deep Linear Networks.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Certified Quantization Strategy Synthesis for Neural Networks.
Proceedings of the Formal Methods - 26th International Symposium, 2024

Towards Efficient Verification of Quantized Neural Networks.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Compositional Verification of Efficient Masking Countermeasures against Side-Channel Attacks.
Proc. ACM Program. Lang., October, 2023

Precise Quantitative Analysis of Binarized Neural Networks: A BDD-based Approach.
ACM Trans. Softw. Eng. Methodol., May, 2023

A Theory of Unimodal Bias in Multimodal Learning.
CoRR, 2023

QFA2SR: Query-Free Adversarial Transfer Attacks to Speaker Recognition Systems.
Proceedings of the 32nd USENIX Security Symposium, 2023

QEBVerif: Quantization Error Bound Verification of Neural Networks.
Proceedings of the Computer Aided Verification - 35th International Conference, 2023

2022
QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks.
CoRR, 2022

CLEVEREST: Accelerating CEGAR-based Neural Network Verification via Adversarial Attacks.
Proceedings of the Static Analysis - 29th International Symposium, 2022

QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks.
Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, 2022

2021
BDD4BNN: A BDD-Based Quantitative Analysis Framework for Binarized Neural Networks.
Proceedings of the Computer Aided Verification - 33rd International Conference, 2021

2019
Making Agents' Abilities Explicit.
IEEE Access, 2019

Probabilistic Alternating-Time <i>µ</i>-Calculus.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019


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