2 citations · 4 across the 8 of their papers we have counts for
8 papers
LIBRA: Language Model Informed Bandit Recourse Algorithm for Personalized Treatment Planning
Junyu Cao, Ruijiang Gao, Esmaeil Keyvanshokooh +1
We introduce a unified framework that seamlessly integrates algorithmic recourse, contextual bandits, and large language models (LLMs) to support sequential decision-making in high…
Beyond Demand Estimation: Consumer Surplus Evaluation via Cumulative Propensity Weights
Zeyu Bian, Max Biggs, Ruijiang Gao +1
This paper develops a practical framework for using observational data to audit the consumer surplus effects of AI-driven decisions, specifically in targeted pricing and algorithmi…
Sell Data to AI Algorithms Without Revealing It: Secure Data Valuation and Sharing via Homomorphic Encryption
Michael Yang, Ruijiang Gao, Zhiqiang Zheng
The rapid expansion of Artificial Intelligence is hindered by a fundamental friction in data markets: the value-privacy dilemma, where buyers cannot verify a dataset's utility with…
Revealing AI Reasoning Increases Trust but Crowds Out Unique Human Knowledge
Zenan Chen, Ruijiang Gao, Yingzhi Liang
Effective human-AI collaboration requires humans to accurately gauge AI capabilities and calibrate their trust accordingly. Humans often have context-dependent private information,…
HR-Bandit: Human-AI Collaborated Linear Recourse Bandit
Junyu Cao, Ruijiang Gao, Esmaeil Keyvanshokooh
Human doctors frequently recommend actionable recourses that allow patients to modify their conditions to access more effective treatments. Inspired by such healthcare scenarios, w…
Sentinel-Guided Zero-Shot Learning: A Collaborative Paradigm without Real Data Exposure
Fan Wan, Xingyu Miao, Haoran Duan +3
With increasing concerns over data privacy and model copyrights, especially in the context of collaborations between AI service providers and data owners, an innovative SG-ZSL para…
Computational Copyright: Towards A Royalty Model for Music Generative AI
Junwei Deng, Xirui Jiang, Shiyuan Zhang +5
The rapid rise of generative AI has intensified copyright and economic tensions in creative industries, particularly in music. Current approaches addressing this challenge often fo…
Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
Hao Wang, Luxi He, Rui Gao +1
Machine learning (ML) models can underperform on certain population groups due to choices made during model development and bias inherent in the data. We categorize sources of disc…