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# Robust Meta-learning for Mixed Linear Regression with Small Batches | ||
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This project contains the code for the paper accepted at NeurIPS 2020 with the above title. The file `RPCA.py` contains an implementation of the algorithm and the simulations done in the paper. This project also contains the code of its preceeding [ICML 2020 paper](https://arxiv.org/abs/2002.08936) where we provide the base code for an implementation of our end-to-end meta learning algorithm for a mixture of linear regression in the file `meta_learning_utils.py`. As a special case, in the file `meta_learning_sine.py` we simulate this algorithm on a mixture of sine wave reconstruction problem which is a generalization of linear regression under orthonormal polynomial feturization. | ||
This project contains the code for the paper accepted at NeurIPS 2020 with the above title. The file `RPCA.py` contains an implementation of the algorithm and the simulations done in the paper. This project also contains the code of its preceeding [ICML 2020 paper](https://arxiv.org/abs/2002.08936) where we provide the base code for an implementation of our end-to-end meta learning algorithm for a mixture of linear regression in the file `meta_learning_utils.py`. As a interesting case, in the file `meta_learning_sine.py` we simulate this algorithm on a mixture of sine wave reconstruction problem which is a generalization of linear regression under orthonormal polynomial feturization. |