Meta-Learning via Learned Loss (1906.05374v4)

Abstract: Typically, loss functions, regularization mechanisms and other important aspects of training parametric models are chosen heuristically from a limited set of options. In this paper, we take the first step towards automating this process, with the view of producing models which train faster and more robustly. Concretely, we present a meta-learning method for learning parametric loss functions that can generalize across different tasks and model architectures. We develop a pipeline for meta-training such loss functions, targeted at maximizing the performance of the model trained under them. The loss landscape produced by our learned losses significantly improves upon the original task-specific losses in both supervised and reinforcement learning tasks. Furthermore, we show that our meta-learning framework is flexible enough to incorporate additional information at meta-train time. This information shapes the learned loss function such that the environment does not need to provide this information during meta-test time. We make our code available at https://sites.google.com/view/mlthree.

• The paper introduces a meta-loss framework that learns high-dimensional, differentiable loss functions via back-propagation, outperforming traditional task-specific losses.
• It incorporates extra meta-train information, like exploratory data and expert demonstrations, to shape the learned loss without needing these signals during meta-test time.
• Experimental results demonstrate improved convergence and sample efficiency in both supervised and reinforcement learning tasks across diverse model architectures.

Overview of Meta Learning via Learned Loss

The paper entitled "Meta Learning via Learned Loss" addresses a significant gap in machine learning, specifically in automating the determination of loss functions which are traditionally chosen heuristically. The authors present a novel meta-learning framework that learns parametric loss functions capable of generalizing across various tasks and model architectures. This framework endeavors to enhance the training efficiency and robustness of models by embedding adaptable loss landscapes that foster optimal model performance irrespective of the task.

Key Contributions

The primary contributions of the work are encapsulated in two major areas:

1. Meta-Loss Framework: The authors introduce a framework capable of learning high-dimensional loss functions through back-propagation. This approach demonstrates superior performance when models are optimized using these learned losses compared to direct optimization through task-specific losses. Importantly, the meta-losses maintain the generality characteristic of traditional task losses while transcending their limitations.
2. Incorporation of Extra Meta-Train Information: The proposed framework is sufficiently flexible to integrate extra information at meta-train time. This allows the learned loss functions to be shaped by additional signals, such as exploratory data or expert demonstrations, without requiring these signals during meta-test time. This novel capability enables models to train solely using learned meta-losses, thereby discovering more efficient optimization strategies.

Methodology

The paper categorizes meta-learning approaches into two types: representation learning and optimization learning. The focus of this paper is on the optimization learning type, where the authors propose learning any parametric loss function whose output is differentiable concerning its parameters.

The methodology involves two interconnected optimization loops—inner and outer loops. Within the inner loop, a model is trained using the learned meta-loss function via gradient descent. In the outer loop, the meta-loss itself is optimized by minimizing a predefined task-loss, traditionally used for various learning tasks.

Experimental Evaluation

The robustness and generalization capabilities of the learned meta-loss framework are empirically evaluated across a diverse set of problems:

• Supervised Learning: Tasks include regression and classification, where the meta-loss demonstrates superior performance in terms of convergence and generalization on unseen tasks.
• Reinforcement Learning: Both model-based and model-free settings are explored. In model-based scenarios, the learned meta-loss outperformed traditional task-losses by promoting swifter policy optimization on test environments without requiring access to the dynamics model. Similarly, in model-free settings, meta-losses showed significantly better sample efficiency.

Additionally, the authors illustrate the utility of adding extra loss information during meta-train time to enhance optimization efficiency. For instance, physics priors and intermediate goal states were incorporated, demonstrating the potential for faster convergence and more effective exploration in reinforcement learning environments.

Implications and Future Perspectives

The implications of this research are profound. The ability of models to generalize loss functions across tasks could substantially reduce the manual intervention traditionally required in constructing loss functions. Moreover, the integration of additional task-specific information into the meta-loss framework opens avenues for incorporating domain knowledge effectively, potentially accelerating learning in complex environments.

Looking ahead, this framework lays the foundation for developing more autonomous machine learning systems. Future research could delve into synthesizing and combining multiple learned meta-loss functions, thereby enhancing adaptability across distinct task families. Additionally, extending the exploration of curiosity-driven signals during meta-train time could cultivate deeply intelligent systems capable of autonomous exploration and learning.

In conclusion, the paper by Bechtle et al. offers a compelling stride in the domain of meta-learning, presenting a potent framework that not only achieves efficient optimization across varying model architectures but also embodies the versatility to exploit additional learning signals.