unlearning and Model Editing workshoP

This workshop provides a venue for original scientific work presenting novel model editing techniques. We also encourage submissions on model compression methods, which aim to minimize the storage and computational costs of operating trained models with little impact on their performance. Additionally, papers that explore methods for adapting foundation models for efficient fine-tuning or editing are relevant.

Workshop topics: We solicit papers on topics including, but not limited to:

• Unlearning

• Model Stitching and Editing

• Model compression

• Efficient domain adaptation 

• Multi-domain/cross-domain U&ME

• Online/lifelong learning, unlearning, and model editing

• Responsible U&ME (e.g., robustness, ethics and fairness, resource efficiency, privacy, and regulatory compliance)  

• Applications in computer vision 

Though we prioritize papers on generative models, but we also welcome submissions that present methods designed for other types of models, such as discriminative classifiers.