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On the Limitations and Prospects of Machine Unlearning for Generative AI
Generative AI (GenAI), which aims to synthesize realistic and diverse data samples from latent variables or other data modalities, has achieved remarkable results in various domains, such as natural language, images, audio, and graphs.
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
Current text-to-image diffusion models have achieved groundbreaking results in image generation tasks.
Improving Generalization of Meta-Learning With Inverted Regularization at Inner-Level
Despite the broad interest in meta-learning, the generalization problem remains one of the significant challenges in this field.
Meta Learning with Minimax Regularization
Even though meta-learning has attracted research wide attention in recent years, the generalization problem of meta-learning is still not well addressed.
Online Continual Adaptation with Active Self-Training
Our theoretical results show that OSAMD can fast adapt to changing environments with active queries.
