1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
Michal Lukasik, Lin Chen, Harikrishna Narasimhan +7
Bipartite ranking is a fundamental supervised learning problem, with the goal of learning a ranking over instances with maximal Area Under the ROC Curve (AUC) against a single bina…
cs.LG2024
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
Adel Javanmard, Lin Chen, Vahab Mirrokni +2
Due to the rise of privacy concerns, in many practical applications the training data is aggregated before being shared with the learner, in order to protect privacy of users' sens…
cs.LG2023★ 1 cited
Learning from Aggregated Data: Curated Bags versus Random Bags
Lin Chen, Gang Fu, Amin Karbasi +1
Protecting user privacy is a major concern for many machine learning systems that are deployed at scale and collect from a diverse set of population. One way to address this concer…