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20192026
most citedPrivacy Preserving Vertical Federated Learning for Tree-based Models

185 citations · 374 across the 23 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.DB2024★ 1 cited

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…

cs.LG2024

VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection

Fei Xiao, Shaofeng Cai, Gang Chen +3

Fraud detection presents a challenging task characterized by ever-evolving fraud patterns and scarce labeled data. Existing methods predominantly rely on graph-based or sequence-ba…

cs.DB2024★ 20 cited

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…

cs.DB2024★ 1 cited

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…

cs.CV2024★ 1 cited

Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection

Jiaqi Zhu, Shaofeng Cai, Fang Deng +2

Large vision-language models (LVLMs) are markedly proficient in deriving visual representations guided by natural language. Recent explorations have utilized LVLMs to tackle zero-s…

cs.LG2024

pTNAS: Progressive Neural Architecture Search for Tabular Data

Naili Xing, Shaofeng Cai, Lingze Zeng +4

Recent advances have shifted the paradigm of tabular learning toward tabular foundation models, yet their accuracy relies on a heavy inference cost that scales poorly with context…