Rethinking Fairness Representation in Multi-Task Learning: a Performance-Informed Variance Reduction Approach
Keywords: Multi-Task Learning, Fair Optimization, Dynamic Weighting Strategy
Abstract: Multi-task learning (MTL) can leverage shared knowledge across tasks to improve data efficiency and generalization performance, and has been applied in various scenarios. However, task imbalance remains a major challenge for existing MTL methods. While the prior works have attempted to mitigate inter-task unfairness through loss-based and gradient-based strategies, they still exhibit imbalanced performance across tasks on common benchmarks.
This key observation motivates us to consider performance-level information as an explicit fairness indicator, which can more accurately reflect the current optimization status of each task, and accordingly help to adjust the gradient aggregation process.
Specifically, we utilize the performance variance among tasks as the fairness indicator and introduce a dynamic weighting strategy to gradually reduce the performance variance.
Based on this, we propose PIVRG, a novel performance-informed variance reduction gradient aggregation approach.
Extensive experiments show that PIVRG achieves state-of-the-art performance across various benchmarks, spanning both supervised learning and reinforcement learning tasks with task numbers ranging from 2 to 40. Results from the ablation study also show that our approach can be integrated into existing methods, significantly enhancing their performance while reducing the variance in task performance, thus achieving fairer optimization.
Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 5633
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