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cs.LG2026
SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) Models
Mingyu Lu, Soham Gadgil, Chris Lin +2
As Text-to-Image (T2I) diffusion models are increasingly used in real-world creative workflows, a principled framework for valuing contributors who provide a collection of data is…
cs.LG2025
CellCLIP -- Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning
Mingyu Lu, Ethan Weinberger, Chanwoo Kim +1
High-content screening (HCS) assays based on high-throughput microscopy techniques such as Cell Painting have enabled the interrogation of cells' morphological responses to perturb…
cs.LG2025
An Efficient Framework for Crediting Data Contributors of Diffusion Models
Chris Lin, Mingyu Lu, Chanwoo Kim +1
As diffusion models are deployed in real-world settings, and their performance is driven by training data, appraising the contribution of data contributors is crucial to creating i…