most citedQuantifying Correlations of Machine Learning Models

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Copyright Infringement Risk Reduction via Chain-of-Thought and Task Instruction Prompting

Neeraj Sarna, Yuanyuan Li, Michael von Gablenz

Large scale text-to-image generation models can memorize and reproduce their training dataset. Since the training dataset often contains copyrighted material, reproduction of train…

cs.LG2025

Counterfactually Fair Conformal Prediction

Ozgur Guldogan, Neeraj Sarna, Yuanyuan Li +1

While counterfactual fairness of point predictors is well studied, its extension to prediction sets--central to fair decision-making under uncertainty--remains underexplored. On th…

cs.CV2025

Safer Prompts: Reducing Risks from Memorization in Visual Generative AI

Lena Reissinger, Yuanyuan Li, Anna-Carolina Haensch +1

Visual Generative AI models have demonstrated remarkable capability in generating high-quality images from user inputs like text prompts. However, because these models have billion…

cs.LG20251 cited

Quantifying Correlations of Machine Learning Models

Yuanyuan Li, Neeraj Sarna, Yang Lin

Machine Learning models are being extensively used in safety critical applications where errors from these models could cause harm to the user. Such risks are amplified when multip…

cs.LG2024

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms

Disha Ghandwani, Neeraj Sarna, Yuanyuan Li +1

Advanced classification algorithms are being increasingly used in safety-critical applications like health-care, engineering, etc. In such applications, miss-classifications made b…