20 citations · 32 across the 11 of their papers we have counts for
12 papers
SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift
Jiaqi Zhu, Xincheng Chen, Yuncheng Wu +2
Generative models trained on a source domain often produce samples that are poorly aligned with shifted target domains, limiting their effectiveness for target-domain data augmenta…
Image Prompt Reconstruction Attacks on Distributed MLLM Inference Frameworks
Xinjian Luo, Hongyan Chang, Jianxin Wei +5
Distributed large language model (LLM) inference frameworks connect isolated consumer-grade devices for large-scale model inference, substantially reducing hardware constraints. Ho…
From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics
Lingze Zeng, Shaofeng Cai, Changshuo Liu +3
Relational data stored in RDBMS is foundational to many real-world applications across domains such as e-commerce, finance, and sociality. While deep neural networks (DNNs) have ac…
Towards Effective Orchestration of AI x DB Workloads
Naili Xing, Haotian Gao, Zhanhao Zhao +6
AI-driven analytics are increasingly crucial to data-centric decision-making. Executing relational and AI operators in separate runtimes prevents the database optimizer and runtime…
Modeling Concurrency Control as a Learnable Function
Hexiang Pan, Shaofeng Cai, Tien Tuan Anh Dinh +4
Concurrency control (CC) algorithms are important in modern transactional databases, as they enable high performance by executing transactions concurrently while ensuring correctne…
SeSeMI: Secure Serverless Model Inference on Sensitive Data
Guoyu Hu, Yuncheng Wu, Gang Chen +2
Model inference systems are essential for implementing end-to-end data analytics pipelines that deliver the benefits of machine learning models to users. Existing cloud-based model…
NeurDB: On the Design and Implementation of an AI-powered Autonomous Database
Zhanhao Zhao, Shaofeng Cai, Haotian Gao +8
Databases are increasingly embracing AI to provide autonomous system optimization and intelligent in-database analytics, aiming to relieve end-user burdens across various industry…
NeurDB: An AI-powered Autonomous Data System
Beng Chin Ooi, Shaofeng Cai, Gang Chen +9
In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises…
Powering In-Database Dynamic Model Slicing for Structured Data Analytics
Lingze Zeng, Naili Xing, Shaofeng Cai +4
Relational database management systems (RDBMS) are widely used for the storage of structured data. To derive insights beyond statistical aggregation, we typically have to extract s…
Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective
Xinjian Luo, Yangfan Jiang, Fei Wei +3
Diffusion models have recently gained significant attention in both academia and industry due to their impressive generative performance in terms of both sampling quality and distr…
Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs
Yizheng Zhu, Yuncheng Wu, Zhaojing Luo +2
Organizations are increasingly recognizing the value of data collaboration for data analytics purposes. Yet, stringent data protection laws prohibit the direct exchange of raw data…
Passive Inference Attacks on Split Learning via Adversarial Regularization
Xiaochen Zhu, Xinjian Luo, Yuncheng Wu +3
Split Learning (SL) has emerged as a practical and efficient alternative to traditional federated learning. While previous attempts to attack SL have often relied on overly strong…