tasks are monotone and submodular, we show strong theoretical guarantees for our proposed methods even though the training objective may not be submodular. We also demonstrate the effectiveness of our framework on two real-world problem instances where we observe that our methods lead to a significant reduction in computational complexity in solving the new tasks while incurring a small performance loss compared to when the tasks are fully optimized.'
author:
- 'Arman Adibi[^1] ,Aryan Mokhtari[^2] ,Hamed Hassani[^3]'
bibliography:
- 'ref.bib'
- 'ref\_2.bib'
title: ' Submodular Meta-Learning '
---

Introduction
============

Many applications in artificial intelligence necessitate exploiting prior data and experience to enhance quality and efficiency on new tasks. This is often manifested through a set of tasks given in the training phase from which we can learn a model or representation that can be later used for new unseen tasks in the test phase. In this regard, meta-learning aims at exploiting the data from the available tasks to learn model parameters or representation that can be later used to perform well on new unseen tasks, in particular, when we have access to limited data and computational power at the test time [@thrun2012learning; @schmidhuber1992learning; @bengio1990learning; @vilalta2002perspective]. By now, there are 