2 citations · 2 across the 8 of their papers we have counts for
9 papers
How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews
Riley Grossman, Songjiang Liu, Michael K. Chen +3
Generative AI is being increasingly integrated into web search for the convenience it provides users. In this work, we aim to understand how generative AI disrupts web search by re…
Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics
Khang Tran, Khoa Nguyen, Cristian Borcea +1
Recent advances in large language models for test case generation have improved branch coverage via prompt-engineered mutations. However, they still lack principled mechanisms for…
AdFL: In-Browser Federated Learning for Online Advertisement
Ahmad Alemari, Pritam Sen, Cristian Borcea
Since most countries are coming up with online privacy regulations, such as GDPR in the EU, online publishers need to find a balance between revenue from targeted advertisement and…
NOIR: Privacy-Preserving Generation of Code with Open-Source LLMs
Khoa Nguyen, Khiem Ton, NhatHai Phan +6
Although boosting software development performance, large language model (LLM)-powered code generation introduces intellectual property and data security risks rooted in the fact t…
Inconsistencies in Classification of Online News Articles: A Call for Common Standards in Brand Safety Services
Michael Smith, Riley Grossman, Antonio Torres-Aguero +3
This study examines inconsistencies in the brand safety classifications of online news articles by analyzing ratings from three leading brand safety providers, DoubleVerify, Integr…
SGFusion: Stochastic Geographic Gradient Fusion in Federated Learning
Khoa Nguyen, Khang Tran, NhatHai Phan +3
This paper proposes Stochastic Geographic Gradient Fusion (SGFusion), a novel training algorithm to leverage the geographic information of mobile users in Federated Learning (FL).…
FedUP: Efficient Pruning-based Federated Unlearning for Model Poisoning Attacks
Nicolò Romandini, Cristian Borcea, Rebecca Montanari +1
Federated Learning (FL) can be vulnerable to attacks, such as model poisoning, where adversaries send malicious local weights to compromise the global model. Federated Unlearning (…
FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning
Phung Lai, Xiaopeng Jiang, Hai Phan +5
Federated Learning (FL) allows collaborative training among multiple devices without data sharing, thus enabling privacy-sensitive applications on mobile or Internet of Things (IoT…
Demo: SGCode: A Flexible Prompt-Optimizing System for Secure Generation of Code
Khiem Ton, Nhi Nguyen, Mahmoud Nazzal +6
This paper introduces SGCode, a flexible prompt-optimizing system to generate secure code with large language models (LLMs). SGCode integrates recent prompt-optimization approaches…