Trustworthy Hypothesis Transfer Learning

可信假设迁移学习

• 批准号: DE240101089
• 负责人: Dr Feng Liu
• 金额: $ 30.28万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE240101089
• 关键词: Trustworthy; Hypothesis; Transfer; Learning

It is urgent to develop a new hypothesis transfer learning scheme that can overcome potential risks when finetuning unreliable large-scale pre-trained models. This project aims to develop an advanced and reliable scheme of hypothesis transfer learning, called Trustworthy Hypothesis Transfer Learning (TrustHTL). A new theoretically guaranteed heterogeneous hypothesis transfer learning framework will be developed to handle heterogeneous situations; a methodology to disinherit risks of pre-trained models and a new fuzzy relation based distributional discrepancy in heterogeneous transfer learning scenarios. The outcomes should significantly improve the reliability of machine learning with benefits for safety learning in data analytics.

迫切需要开发一种新的假设迁移学习方案，以克服在微调不可靠的大规模预训练模型时的潜在风险。本项目旨在开发一种先进可靠的假设迁移学习方案，称为可信假设迁移学习（TrustHTL）。将开发一个新的理论上有保证的异构假设迁移学习框架来处理异构情况;一种方法来消除预训练模型的风险，以及一种新的基于模糊关系的异构迁移学习场景中的分布差异。这些结果将显著提高机器学习的可靠性，并有利于数据分析中的安全学习。