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Biased regularization and fine-tuning are two recent meta-learningapproaches. They have been shown to be effective to tackle distributions oftasks, in which the tasks' target vectors are all close to a commonmeta-parameter vector. However, these methods may perform poorly onheterogeneous environments of tasks, where the complexity of the tasks'distribution cannot be captured by a single meta-parameter vector. We addressthis limitation by conditional meta-learning, inferring a conditioning functionmapping task's side information into a meta-parameter vector that isappropriate for that task at hand. We characterize properties of theenvironment under which the conditional approach brings a substantial advantageover standard meta-learning and we highlight examples of environments, such asthose with multiple clusters, satisfying these properties. We then propose aconvex meta-algorithm providing a comparable advantage also in practice.Numerical experiments confirm our theoretical findings.

The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning