Meta-learning aims at optimizing the hyperparameters of a model class or training algorithm
from the observation of data from a number of related tasks. Following the setting of Baxter …

Meta-learning optimizes an inductive bias—typically in the form of the hyperparameters of a
base-learning algorithm—by observing data from a finite number of related tasks. This paper …

Meta-learning automatically infers an inductive bias by observing data from a number of
related tasks. The inductive bias is encoded by hyperparameters that determine aspects of …

Supposing the $ n $ training tasks and the new task are sampled from the same
environment, traditional meta learning theory derives an error bound on the expected loss …

Many recent theoretical works on\emph {meta-learning} aim to achieve guarantees in
leveraging similar representational structures from related tasks towards simplifying a target …

Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from
data corresponding to multiple related tasks with the goal of improving the sample efficiency …

Recent work has established that the conditional mutual information (CMI) framework of
Steinke and Zakynthinou (2020) is expressive enough to capture generalization guarantees …

Meta learning automatically infers an inductive bias, that includes the hyperparameter of the
base-learning algorithm, by observing data from a finite number of related tasks. This paper …

Meta-learning leverages related source tasks to learn an initialization that can be quickly
fine-tuned to a target task with limited labeled examples. However, many popular meta …

Meta learning automatically infers an inductive bias, that includes the hyperparameter of the
baselearning algorithm, by observing data from a finite number of related tasks. This paper …