How Does the Task Landscape Affect MAML Performance?

• URL: http://arxiv.org/abs/2010.14672v5
• Date: Tue, 9 Aug 2022 18:42:42 GMT
• Title: How Does the Task Landscape Affect MAML Performance?
• Authors: Liam Collins, Aryan Mokhtari, Sanjay Shakkottai
• Abstract summary: We show that Model-Agnostic Meta-Learning (MAML) is more difficult to optimize than non-adaptive learning (NAL) We analytically address this issue in a linear regression setting consisting of a mixture of easy and hard tasks. We also give numerical and analytical results suggesting that these insights apply to two-layer neural networks.
• Score: 42.27488241647739
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. However, the MAML objective is significantly more difficult to optimize compared to standard non-adaptive learning (NAL), and little is understood about how much MAML improves over NAL in terms of the fast adaptability of their solutions in various scenarios. We analytically address this issue in a linear regression setting consisting of a mixture of easy and hard tasks, where hardness is related to the rate that gradient descent converges on the task. Specifically, we prove that in order for MAML to achieve substantial gain over NAL, (i) there must be some discrepancy in hardness among the tasks, and (ii) the optimal solutions of the hard tasks must be closely packed with the center far from the center of the easy tasks optimal solutions. We also give numerical and analytical results suggesting that these insights apply to two-layer neural networks. Finally, we provide few-shot image classification experiments that support our insights for when MAML should be used and emphasize the importance of training MAML on hard tasks in practice.

Related papers

• MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning [43.512739869120125]
  We propose MAML-en-LLM, a novel method for meta-training large language models (LLMs) MAML-en-LLM can learn truly generalizable parameters that not only perform well on disjointed tasks but also adapts to unseen tasks. We demonstrate that MAML-en-LLM outperforms baselines in settings with limited amount of training data on both seen and unseen domains.
  arXiv  Detail & Related papers  (2024-05-19T04:49:42Z)
• Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning [59.38343286807997]
  We propose Model-Agnostic Multitask Fine-tuning (MAMF) for vision-language models on unseen tasks. Compared with model-agnostic meta-learning (MAML), MAMF discards the bi-level optimization and uses only first-order gradients. We show that MAMF consistently outperforms the classical fine-tuning method for few-shot transfer learning on five benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-09T17:26:53Z)
• ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning [12.215288736524268]
  This paper proposes a novel method, ST-MAML, that empowers model-agnostic meta-learning (MAML) to learn from multiple task distributions. We demonstrate that ST-MAML matches or outperforms the state-of-the-art on two few-shot image classification tasks, one curve regression benchmark, one image completion problem, and a real-world temperature prediction application.
  arXiv  Detail & Related papers  (2021-09-27T18:54:50Z)
• SLAW: Scaled Loss Approximate Weighting for Efficient Multi-Task Learning [0.0]
  Multi-task learning (MTL) is a subfield of machine learning with important applications. The best MTL optimization methods require individually computing the gradient of each task's loss function. We propose Scaled Loss Approximate Weighting (SLAW), a method for multi-task optimization that matches the performance of the best existing methods while being much more efficient.
  arXiv  Detail & Related papers  (2021-09-16T20:58:40Z)
• MAML is a Noisy Contrastive Learner [72.04430033118426]
  Model-agnostic meta-learning (MAML) is one of the most popular and widely-adopted meta-learning algorithms nowadays. We provide a new perspective to the working mechanism of MAML and discover that: MAML is analogous to a meta-learner using a supervised contrastive objective function. We propose a simple but effective technique, zeroing trick, to alleviate such interference.
  arXiv  Detail & Related papers  (2021-06-29T12:52:26Z)
• Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning [56.17603785248675]
  Model-agnostic meta-learning (MAML) has become a popular research area. Existing MAML algorithms rely on the episode' idea by sampling a few tasks and data points to update the meta-model at each iteration. This paper proposes memory-based algorithms for MAML that converge with vanishing error.
  arXiv  Detail & Related papers  (2021-06-09T08:47:58Z)
• Robust MAML: Prioritization task buffer with adaptive learning process for model-agnostic meta-learning [15.894925018423665]
  Model agnostic meta-learning (MAML) is a popular state-of-the-art meta-learning algorithm. This paper proposes a more robust MAML based on an adaptive learning scheme and a prioritization task buffer. Experimental results on meta reinforcement learning environments demonstrate a substantial performance gain.
  arXiv  Detail & Related papers  (2021-03-15T09:34:34Z)
• On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning [100.14809391594109]
  Model-agnostic meta-learning (MAML) has emerged as one of the most successful meta-learning techniques in few-shot learning. Despite the generalization power of the meta-model, it remains elusive that how adversarial robustness can be maintained by MAML in few-shot learning. We propose a general but easily-optimized robustness-regularized meta-learning framework, which allows the use of unlabeled data augmentation, fast adversarial attack generation, and computationally-light fine-tuning.
  arXiv  Detail & Related papers  (2021-02-20T22:03:04Z)
• A Nested Bi-level Optimization Framework for Robust Few Shot Learning [10.147225934340877]
  NestedMAML learns to assign weights to training tasks or instances. Experiments on synthetic and real-world datasets demonstrate that NestedMAML efficiently mitigates the effects of "unwanted" tasks or instances.
  arXiv  Detail & Related papers  (2020-11-13T06:41:22Z)
• Meta-Learning with Adaptive Hyperparameters [55.182841228303225]
  We focus on a complementary factor in MAML framework, inner-loop optimization (or fast adaptation) We propose a new weight update rule that greatly enhances the fast adaptation process.
  arXiv  Detail & Related papers  (2020-10-31T08:05:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.