4 citations · 7 across the 7 of their papers we have counts for
7 papers
Persuasion Propagation: Does Persuasion Change AI Agent Behavior?
Hyejun Jeong, Amir Houmansadr, Shlomo Zilberstein +1
AI agents combine normal conversation with autonomous task execution, allowing earlier context to shape how later tasks are executed. Yet studying this possibility is challenging b…
Network-Level Prompt and Trait Leakage in Local Research Agents
Hyejun Jeong, Mohammadreza Teymoorianfard, Abhinav Kumar +2
We show that Web and Research Agents (WRAs) -- language-model-based systems that investigate complex topics on the Internet -- are vulnerable to inference attacks by passive networ…
VIDSTAMP: A Temporally-Aware Watermark for Ownership and Integrity in Video Diffusion Models
Mohammadreza Teymoorianfard, Siddarth Sitaraman, Shiqing Ma +1
Video diffusion models can generate realistic and temporally consistent videos. This raises concerns about provenance, ownership, and integrity. Watermarking can help address these…
ULTra: Unveiling Latent Token Interpretability in Transformer-Based Understanding and Segmentation
Hesam Hosseini, Ghazal Hosseini Mighan, Amirabbas Afzali +2
Transformers have revolutionized Computer Vision (CV) through self-attention mechanisms. However, their complexity makes latent token representations difficult to interpret. We int…
Bias Similarity Measurement: A Black-Box Audit of Fairness Across LLMs
Hyejun Jeong, Shiqing Ma, Amir Houmansadr
Large Language Models (LLMs) reproduce social biases, yet prevailing evaluations score models in isolation, obscuring how biases persist across families and releases. We introduce…
MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification
Sajjad Amini, Mohammadreza Teymoorianfard, Shiqing Ma +1
We present a simple yet effective method to improve the robustness of both Convolutional and attention-based Neural Networks against adversarial examples by post-processing an adve…
A Survey on Federated Unlearning: Challenges and Opportunities
Hyejun Jeong, Shiqing Ma, Amir Houmansadr
Federated learning (FL), introduced in 2017, facilitates collaborative learning between non-trusting parties with no need for the parties to explicitly share their data among thems…