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Found 4 papers, 2 papers with code
Fair Resource Allocation in Multi-Task Learning
Inspired by fair resource allocation in communication networks, we formulate the optimization of MTL as a utility maximization problem, where the loss decreases across tasks are maximized under different fairness measurements.
Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms
In this paper, we propose a new direction-oriented multi-objective problem by regularizing the common descent direction within a neighborhood of a direction that optimizes a linear combination of objectives such as the average loss in MTL.
Interleaving Learning, with Application to Neural Architecture Search
Inspired by the interleaving learning technique of humans, in this paper we explore whether this learning methodology is beneficial for improving the performance of machine learning models as well.
Skillearn: Machine Learning Inspired by Humans' Learning Skills
To achieve this goal, we develop a general framework -- Skillearn, which provides a principled way to represent humans' learning skills mathematically and use the formally-represented skills to improve the training of ML models.
