Advanced Machine Learning with Bilevel Optimization

迫切需要开发一种新的机器学习（ML）范式，以克服现实世界应用中的数据隐私和模型大小限制。该项目旨在开发一种具有双层优化的ML高级范例，称为双层ML。一个理论上保证快速近似求解器和一个新的模糊双层学习框架将开发，以实现在复杂的情况下的目标;一种方法来转移知识和方法，以快速适应双层优化解决方案时，所需的计算资源的变化。预期的结果将显著提高ML的可靠性，并有利于基于ML的数据分析中的安全学习和计算资源优化。

There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowledge and an approach to fast-adapt bilevel optimization solutions when required computing resources change. The anticipated outcomes should significantly improve the reliability of ML with benefits for safety learning and computing resource optimisation in ML-based data analytics.