Ensemble Prediction of Task Affinity for Efficient Multi-Task Learning
arXiv:2602.18591v1 Announce Type: new Abstract: A fundamental problem in multi-task learning (MTL) is identifying groups of tasks that should be learned together. Since training MTL models for all possible combinations of tasks is prohibitively expensive for large task sets, a crucial component of efficient and effective task grouping is predicting whether a group of tasks would benefit from learning together, measured as per-task performance gain over single-task learning. In this paper, we propose ETAP (Ensemble Task Affinity Predictor), a scalable framework that integrates principled and data-driven estimators to predict MTL performance gains. First, we consider the gradient-based updates of shared parameters in an MTL model to measure the affinity between a pair of tasks as the similarity between the parameter updates based on these tasks. This linear estimator, which we call affinity score, naturally extends to estimating affinity within a group of tasks. Second, to refine these
arXiv:2602.18591v1 Announce Type: new Abstract: A fundamental problem in multi-task learning (MTL) is identifying groups of tasks that should be learned together. Since training MTL models for all possible combinations of tasks is prohibitively expensive for large task sets, a crucial component of efficient and effective task grouping is predicting whether a group of tasks would benefit from learning together, measured as per-task performance gain over single-task learning. In this paper, we propose ETAP (Ensemble Task Affinity Predictor), a scalable framework that integrates principled and data-driven estimators to predict MTL performance gains. First, we consider the gradient-based updates of shared parameters in an MTL model to measure the affinity between a pair of tasks as the similarity between the parameter updates based on these tasks. This linear estimator, which we call affinity score, naturally extends to estimating affinity within a group of tasks. Second, to refine these estimates, we train predictors that apply non-linear transformations and correct residual errors, capturing complex and non-linear task relationships. We train these predictors on a limited number of task groups for which we obtain ground-truth gain values via multi-task learning for each group. We demonstrate on benchmark datasets that ETAP improves MTL gain prediction and enables more effective task grouping, outperforming state-of-the-art baselines across diverse application domains.
Executive Summary
This article proposes ETAP, a scalable framework for predicting task affinity in multi-task learning (MTL). ETAP integrates linear and non-linear estimators to predict MTL performance gains, significantly improving task grouping. By leveraging gradient-based updates and non-linear transformations, ETAP accurately estimates task relationships. The authors demonstrate ETAP's effectiveness on benchmark datasets, outperforming state-of-the-art baselines across diverse application domains. With its ability to refine estimates and correct residual errors, ETAP has the potential to revolutionize MTL. However, its reliance on ground-truth gain values may limit its practical applications.
Key Points
- ▸ ETAP integrates principled and data-driven estimators to predict MTL performance gains.
- ▸ The framework leverages gradient-based updates and non-linear transformations to estimate task relationships.
- ▸ ETAP outperforms state-of-the-art baselines across diverse application domains.
Merits
Strength in Estimation
ETAP's ability to integrate linear and non-linear estimators significantly improves task grouping and MTL performance gain prediction.
Scalability
The framework's scalability allows for efficient analysis of large task sets, making it a practical solution for real-world applications.
Flexibility
ETAP's ability to adapt to diverse application domains demonstrates its flexibility and potential for widespread adoption.
Demerits
Reliance on Ground-Truth Gain Values
The requirement for ground-truth gain values may limit ETAP's practical applications, especially in scenarios where such values are difficult to obtain.
Potential Overfitting
The use of non-linear transformations and predictors may lead to overfitting, especially when working with small datasets or limited task groups.
Expert Commentary
The ETAP framework represents a significant advancement in the field of multi-task learning, offering a scalable and flexible solution for predicting task affinity. By integrating linear and non-linear estimators, ETAP demonstrates its ability to accurately estimate task relationships, even in complex and diverse application domains. While ETAP's reliance on ground-truth gain values and potential for overfitting may limit its practical applications, its potential to improve MTL performance gain prediction and enable more effective task grouping makes it an exciting development in the field. As the field continues to evolve, it will be essential to address these limitations and explore new applications for ETAP.
Recommendations
- ✓ Further research should focus on developing more robust and efficient methods for obtaining ground-truth gain values, potentially leveraging alternative sources or approximations.
- ✓ To mitigate the risk of overfitting, researchers should explore regularization techniques and ensemble methods to improve the generalizability of ETAP's predictions.
