21 citations · 64 across the 20 of their papers we have counts for
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Learning Who Benefits Most: Multi-Treatment Causal Ranking for Population Health Stratification
Rocco Zaccagnino, Martina Girolamo, Alberto Lombardi +3
Population health stratification frameworks, such as the Italian DM77, organize patients by clinical need and care pathways but offer limited guidance on which care changes to prio…
Simple Diffusion Language Models Are More Effective Few-Step Generators Than Reported
Hasan Amin, Ming Yin, Rajiv Khanna
Diffusion language models (DLMs) promise fast parallel generation, yet high-quality samples often require large number of refinement steps, which diminishes their advantage in prac…
Graphionale: How Graph Visualizations of LLM Rationales Affect Human Decision Making
Xinru Wang, Zhexuan Ma, Ming Yin +2
Large Language Models (LLMs) are increasingly equipped with augmented reasoning capabilities to generate rationales that support human decision-making. Yet these text-dense rationa…
From Fallback to Frontline: When Can LLMs be Superior Annotators of Human Perspectives?
Hasan Amin, Harry Yizhou Tian, Xiaoni Duan +3
Although large language models (LLMs) are increasingly used as annotators at scale, they are typically treated as a pragmatic fallback rather than a faithful estimator of human per…
Understanding the Effects of AI-Assisted Critical Thinking on Human-AI Decision Making
Harry Yizhou Tian, Hasan Amin, Ming Yin
Despite the growing prevalence of human-AI decision making, the human-AI team's decision performance often remains suboptimal, partially due to insufficient examination of humans'…
Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration
Hasan Amin, Ming Yin, Rajiv Khanna
In human-AI decision making, designing AI that complements human expertise has been a natural strategy to enhance human-AI collaboration, yet it often comes at the cost of decrease…
Human-LLM Collaborative Feature Engineering for Tabular Data
Zhuoyan Li, Aditya Bansal, Jinzhao Li +8
Large language models (LLMs) are increasingly used to automate feature engineering in tabular learning. Given task-specific information, LLMs can propose diverse feature transforma…