TaskMet: Task-Driven Metric Learning for Model Learning
(2312.05250)Abstract
Deep learning models are often deployed in downstream tasks that the training procedure may not be aware of. For example, models solely trained to achieve accurate predictions may struggle to perform well on downstream tasks because seemingly small prediction errors may incur drastic task errors. The standard end-to-end learning approach is to make the task loss differentiable or to introduce a differentiable surrogate that the model can be trained on. In these settings, the task loss needs to be carefully balanced with the prediction loss because they may have conflicting objectives. We propose take the task loss signal one level deeper than the parameters of the model and use it to learn the parameters of the loss function the model is trained on, which can be done by learning a metric in the prediction space. This approach does not alter the optimal prediction model itself, but rather changes the model learning to emphasize the information important for the downstream task. This enables us to achieve the best of both worlds: a prediction model trained in the original prediction space while also being valuable for the desired downstream task. We validate our approach through experiments conducted in two main settings: 1) decision-focused model learning scenarios involving portfolio optimization and budget allocation, and 2) reinforcement learning in noisy environments with distracting states. The source code to reproduce our experiments is available at https://github.com/facebookresearch/taskmet
Overview
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Traditional machine learning models prioritize prediction accuracy but may not perform well on subsequent tasks if critical data sections are overlooked.
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TaskMet introduces a strategy that incorporates task information into a learned metric to guide model training towards task-relevant aspects without compromising predictive power.
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Unlike existing methods, TaskMet does not involve direct task-based losses in model parameter updates but improves task performance through metric learning.
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Experiments show that TaskMet can effectively prioritize essential data features, leading to better performance in decision-focused learning and reinforcement learning scenarios.
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The method is robust and interpretable, requiring minimal tuning while maintaining a balance between accuracy and task performance, but metric learning stability and hyper-parameter tuning need consideration.
Introduction
Machine learning models are conventionally trained to maximize accuracy on a given prediction task. While these models may excel at approximating underlying functions, they often falter when employed in subsequent tasks. This may occur if the model's training does not emphasize the specific sections of data critical for those tasks. A prevailing approach to resolve this issue involves end-to-end learning that employs task-specific losses either by making them differentiable or replacing them with surrogate functions. However, this often requires a delicate balance between focusing on the prediction accuracy and the task performance, with a distinct concern being the overfitting to specific tasks, potentially undermining the model's generalization capabilities.
Metric Learning in Model Training
The paper presents an alternative strategy that embeds the task information into a learned metric without altering the optimal prediction model. By altering the model's loss function through metric learning, the model retains its predictive power while adapting to the utility of downstream tasks. The metric effectively serves as a lens focusing the model training on aspects important for performing the task at hand. This method, which the authors call TaskMet, guides the learning process by emphasizing the significance of certain predictions over others based on their impact on task performance.
Validation through Experiments
TaskMet's effectiveness is demonstrated through two main sets of experiments: decision-focused model learning settings, involving portfolio optimization and budget allocation tasks; and reinforcement learning scenarios with distracting or noisy environments. These experiments establish that TaskMet can discern essential data features and prioritize them accordingly, leading to better performance on downstream tasks compared to traditional methods. In particular, TaskMet shows gains in reducing the discrepancy between what the prediction model deems important and what actually matters for the task.
Conclusion
The paper concludes that TaskMet is a robust method for task-based learning, offering both interpretability and improved performance. It allows for training that is task-informed without the direct interference of task-based losses in model parameter updates. TaskMet stands out as it consistently achieves a high balance of prediction accuracy and task performance across a range of settings without requiring intensive tuning. There is potential for further exploration, particularly in extending this learning approach to multiple task losses or for long-horizon planning tasks in reinforcement learning. However, stability in learning the metric and careful hyper-parameter tuning are highlighted as important considerations for successful implementation.
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