Oct 21, 2024 · In this work, we tackle these scalability challenges of state-of-the-art memorization-based MUL algorithms using a series of memorization-score proxies.
Nov 1, 2024 · Our empirical results show that these proxies can introduce accuracy on par with full memorization-based unlearning while dramatically improving.
NeurIPS 2024: What makes unlearning hard and what to do about it; NeurIPS 2024 FITML Workshop: Scalability of memorization-based machine unlearning. Original ...
Nov 4, 2024 · This paper tackles the critical issue of scaling up machine unlearning, which is essential for developing large, powerful AI models while ...
Nov 28, 2024 · Paper Title: "Scalability of memorization-based machine unlearning" Generated below podcast on this paper with Google's Illuminate.
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What is unlearning in machine learning?
Zhao et al. Scalability of memorization-based machine unlearning, NeurIPS ... Nguyen et al. Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What ...
Nov 28, 2024 · ... Machine unlearning (MUL) algorithms that rely on memorization scores to remove specific data from trained models face severe scalability issues.
Oct 11, 2024 · Machine Unlearning is a relatively new field in machine learning that focuses on selectively removing specific training data points and ...
Dec 9, 2024 · This study investigates MU approaches regarding their accuracy and potential applications.
Machine unlearning is the problem of removing the effect of a subset of training data (the “forget set”) from a trained model e.g. to comply with users' ...