Meta-learning is a general approach to equip machine learning models with the ability to handle few-shot scenarios when dealing with many tasks. Most existing meta-learning methods work based on the assumption that all tasks are of equal importance. However, real-world applications often present heterogeneous tasks characterized by varying difficulty levels, noise in training samples, or being distinctively different from most other tasks. In this paper, we introduce a novel meta-learning method designed to effectively manage such heterogeneous tasks by employing rank-based task-level learning objectives, Heterogeneous Tasks Robust Meta-learning (HeTRoM). HeTRoM is proficient in handling heterogeneous tasks, and it prevents easy tasks from overwhelming the meta-learner. The approach allows for an efficient iterative optimization algorithm based on bi-level optimization, which is then improved by integrating statistical guidance. Our experimental results demonstrate that our method provides flexibility, enabling users to adapt to diverse task settings and enhancing the meta-learner's overall performance.

翻译：元学习是一种通用方法，旨在使机器学习模型具备处理多任务场景下小样本学习的能力。现有元学习方法大多基于所有任务同等重要的假设。然而，实际应用中的任务往往具有异构性，表现为任务难度差异、训练样本噪声干扰或任务特性显著区别于多数其他任务。本文提出一种新颖的元学习方法，通过采用基于排序的任务级学习目标——异构任务鲁棒元学习（HeTRoM），以有效管理此类异构任务。HeTRoM能够熟练处理异构任务，并防止简单任务主导元学习器的训练过程。该方法支持基于双层优化的高效迭代优化算法，并通过集成统计指导机制进一步优化算法性能。实验结果表明，本方法具有灵活性，使用户能够适应多样化的任务设置，并显著提升元学习器的整体性能。