Amortized Bayesian Meta-Learning
Meta-learning, or learning-to-learn, has proven to be a successful strategy in attacking problems in supervised learning and reinforcement learning that involve small amounts of data. State-of-the-art solutions involve learning an initialization and/or learning algorithm using a set of training episodes so that the meta learner can generalize to an evaluation episode quickly. These methods perform well but often lack good quantification of uncertainty, which can be vital to real-world applications when data is lacking. We propose a meta-learning method which efficiently amortizes hierarchical variational inference across tasks, learning a prior distribution over neural network weights so that a few steps of Bayes by Backprop will produce a good task-specific approximate posterior. We show that our method produces good uncertainty estimates on contextual bandit and few-shot learning benchmarks.
 The authors consider meta-learning to learn a prior over neural network weights. This is done via amortized variational inference. This means that a good initialisation of the variational parameters are learned across tasks, such that a good set of hyperparameters per task can be found in a few gradient steps. The proposed approach is evaluated on a toy and several popular benchmarks (like miniImagenet). The topic is timely. The contribution is modest, essentially applying the same idea as the one proposed in MAML to a variational objective, but well executed. The paper is relatively well-written and the contributions clearly stated/motivated. Section 2 and 3 could be written in a more compact way (in particular the math), but it does not harm the flow. The authors conducted a good set of experiments, but are missing comparisons Bayesian versions of MAML.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Amortized Bayesian Meta-Learning
Meta-learning, or learning-to-learn, has proven to be a successful strategy in attacking problems in supervised learning and reinforcement learning that involve small amounts of data. State-of-the-art solutions involve learning an initialization and/or learning algorithm using a set of training episodes so that the meta learner can generalize to an evaluation episode quickly. These methods perform well but often lack good quantification of uncertainty, which can be vital to real-world applications when data is lacking. We propose a meta-learning method which efficiently amortizes hierarchical variational inference across tasks, learning a prior distribution over neural network weights so that a few steps of Bayes by Backprop will produce a good task-specific approximate posterior. We show that our method produces good uncertainty estimates on contextual bandit and few-shot learning benchmarks.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The authors consider meta-learning to learn a prior over neural network weights. This is done via amortized variational inference. This means that a good initialisation of the variational parameters are learned across tasks, such that a good set of hyperparameters per task can be found in a few gradient steps. The proposed approach is evaluated on a toy and several popular benchmarks (like miniImagenet). The topic is timely. The contribution is modest, essentially applying the same idea as the one proposed in MAML to a variational objective, but well executed. The paper is relatively well-written and the contributions clearly stated/motivated. Section 2 and 3 could be written in a more compact way (in particular the math), but it does not harm the flow. The authors conducted a good set of experiments, but are missing comparisons Bayesian versions of MAML.
assistant
 The authors consider meta-learning to learn a prior over neural network weights. This is done via amortized variational inference. This means that a good initialisation of the variational parameters are learned across tasks, such that a good set of hyperparameters per task can be found in a few gradient steps. The proposed approach is evaluated on a toy and several popular benchmarks (like miniImagenet). The topic is timely. The contribution is modest, essentially applying the same idea as the one proposed in MAML to a variational objective, but well executed. The paper is relatively well-written and the contributions clearly stated/motivated. Section 2 and 3 could be written in a more compact way (in particular the math), but it does not harm the flow. The authors conducted a good set of experiments, but are missing comparisons Bayesian versions of MAML.