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20202026
most citedAssisted Learning: A Framework for Multi-Organization Learning

15 citations · 19 across the 13 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science

An Luo, Jin Du, Xun Xian +12

Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) and artificial…

cs.LG2025

Can Agentic AI Match the Performance of Human Data Scientists?

An Luo, Jin Du, Fangqiao Tian +9

Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) have significa…

cs.LG2025

AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science

An Luo, Xun Xian, Jin Du +12

Large language models (LLMs) have advanced the automation of data science workflows. Yet it remains unclear whether they can critically leverage external domain knowledge as human…

cs.LG2022★ 2 cited

A Framework for Understanding Model Extraction Attack and Defense

Xun Xian, Mingyi Hong, Jie Ding

The privacy of machine learning models has become a significant concern in many emerging Machine-Learning-as-a-Service applications, where prediction services based on well-trained…

cs.LG2020

ASCII: ASsisted Classification with Ignorance Interchange

Jiaying Zhou, Xun Xian, Na Li +1

The rapid development in data collecting devices and computation platforms produces an emerging number of agents, each equipped with a unique data modality over a particular popula…

cs.LG2020★ 15 cited

Assisted Learning: A Framework for Multi-Organization Learning

Xun Xian, Xinran Wang, Jie Ding +1

In an increasing number of AI scenarios, collaborations among different organizations or agents (e.g., human and robots, mobile units) are often essential to accomplish an organiza…