adapted to a new task at a low computational cost to obtain a task-specific solution.

-   We present computationally efficient deterministic and randomized meta-greedy algorithms to solve the resulting meta-learning problem. When the tasks are monotone and submodular, we prove that the solution obtained by the deterministic algorithm is at least $0.53$-optimal, and the solution of the randomized algorithm is $(1-1/e-o(1))$-optimal in expectation, where the $o(1)$ term vanishes by the size of the solution. These guarantees are obtained by introducing new techniques, despite that the meta-learning objective is *not* submodular.

-   We study the performance of our proposed meta-learning framework and algorithms for movie recommendation and ride-sharing problems. Our experiments illustrate that the solution of our proposed meta-learning scheme, which chooses a large portion of the solution in the training phase and a small portion adaptively at test time, is very close to the solution obtained by choosing the entire solution at the test time when a new task is revealed.

Related work
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**Continuous Meta-Learning.** Meta-learning has gained considerable attention recently mainly due to its success in few shot learning [@vinyals2016matching; @DBLP:conf/iclr/RaviL17; @snell2017prototypical; @wang2019few] as well as reinforcement learning [@DBLP:journals/corr/DuanSCBSA16; @wang2016learning; @DBLP:conf/iclr/SongGYCPT20; @fallah2020provably]. One of the most successful forms 