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Wasserstein task embedding for measuring task similarities
Xinran Liu1, Yikun Bai1, Yuzhe Lu2
1Computer Science Department, Vanderbilt University, 2201 W End Ave, Nashville, 37235, TN, United States.
Summary
This study introduces a novel, model-agnostic method for measuring task similarities in machine learning using optimal transport theory. This approach enables faster and more effective task comparisons, crucial for transfer and meta-learning applications.
Area of Science:
- Machine Learning
- Computer Science
- Data Science
Background:
- Measuring task similarity is vital for machine learning tasks like transfer, multi-task, continual, and meta-learning.
- Existing methods for task similarity assessment are often architecture-dependent, relying on pre-trained models or forward transfer proxies.
- These limitations hinder efficient and generalizable task comparison across diverse machine learning paradigms.
Purpose of the Study:
- To introduce a novel, model-agnostic, and training-free task embedding method for supervised classification.
- To address the limitations of current architecture-dependent approaches for measuring task similarities.
- To enable efficient handling of datasets with partially disjoint label sets.
Main Methods:
- Leveraging optimal transport theory to define a new task embedding for supervised classification.
- Employing multi-dimensional scaling for label embedding, followed by concatenation with dataset samples.
- Defining dataset distance using the 2-Wasserstein distance between updated samples.
- Utilizing a 2-Wasserstein embedding framework to map tasks into a vector space.
Main Results:
- The proposed task embedding is model-agnostic and training-free, capable of handling disjoint label sets.
- Task comparison using the novel embedding is significantly faster than existing methods like Optimal Transport Dataset Distance (OTDD).
- Numerical experiments demonstrate statistically significant correlations between the proposed distance and forward/backward transfer across image recognition datasets.
Conclusions:
- The developed 2-Wasserstein embedding framework provides an efficient and effective approach for measuring task similarities.
- This model-agnostic method advances the field of machine learning by offering a more generalizable tool for task comparison.
- The findings have implications for improving performance in transfer learning, multi-task learning, and meta-learning applications.
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