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cs.LG2026
Optimize Your Sampling: Tuned Diffusion Sampling with Bayesian Optimization
Travis Zhang, Christian Belardi, Justin Lovelace +4
Sampling from a diffusion model typically requires many forward passes through a large neural network, making generation computationally expensive. While much work has focused on e…
cs.LG2025
Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond
Qizhou Wang, Jin Peng Zhou, Zhanke Zhou +3
Large language models (LLMs) should undergo rigorous audits to identify potential risks, such as copyright and privacy infringements. Once these risks emerge, timely updates are cr…