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  • Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade . . .
    We have studied Langevin unlearning under the assumption of asymmetric data sources, where datasets contain both private and public data Our theoretical analysis demonstrates that this framework fundamentally improves the unlearning-utility trade-off by enabling control over unlearning guarantees through data supplementation rather than noise
  • Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade . . .
    The core idea borrows from differential privacy, where public data is well-established as a standard technique to relax privacy-utility tensions, yet its role in unlearning had remained unexplored ALU formalizes this connection through Langevin dynamics with asymmetric data sources: the method trains on both public (permanently available) and private (subject to unlearning requests) data
  • Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem
    Machine unlearning for large language models (LLMs) aims to remove targeted knowledge while preserving general capability In this paper, we recast LLM unlearning as an asymmetric two-task problem: retention is the primary objective and forgetting is an auxiliary From this perspective, we propose a retention-prioritized gradient synthesis framework that decouples task-specific gradient
  • BLUR: A Bi-Level Optimization Approach for LLM Unlearning
    This hierarchical structure naturally leads to a bi-level optimization formulation where the lower-level objective focuses on minimizing the forget loss, while the upper-level objective aims to maintain the model’s utility
  • Dual asymmetric momentum improves federated class unlearning . . . - Nature
    FedDAM further separates retain and forget optimization through dual-asymmetric momentum, enabling faster forgetting while better preserving retained utility under a fixed unlearning budget
  • ReLearn: Unlearning via Learning for Large Language Models
    Current unlearning methods for large language models usually rely on reverse optimization to reduce target token probabilities However, this paradigm disrupts the subsequent tokens predic- tion, degrading model performance and linguis- tic coherence
  • Search - Microsoft Bing
    Search with Microsoft Bing and use the power of AI to find information, explore webpages, images, videos, maps, and more A smart search engine for the forever curious
  • GATEWAY INTERMEDIATE WORKBOOK - MONROE INSTITUTE OF APPLIED SCIENCES
    RESTRICTION The material contained herein is intended for the personal and private use only by those who have completed the first Session of the Gateway Program The attempted application by others may result in undesir- able and uncontrolled effects detrimental to any such unauthorized user @ 1977 Monroe Institute of Applied Sciences Box 57 · Afton, Virginia 22920 Approved For Release
  • What does SEQ mean? - Abbreviation Finder
    What does SEQ mean? Are you looking for the meanings of SEQ? On the following image, you can see major definitions of SEQ If you want, you can also download image file to print, or you can share it with your friend via Facebook, Twitter, Pinterest, Google, etc To see all meanings of SEQ, please scroll down
  • GitHub - datalust seq-tickets: Issues, design discussions and feature . . .
    Seq Welcome to the home of issues and discussions for Seq, the self-hosted search, analysis, and alerting server built for structured logs and traces





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