# End to end learning and optimization on graphs

@article{Wilder2019EndTE, title={End to end learning and optimization on graphs}, author={Bryan Wilder and Eric Ewing and Bistra N. Dilkina and Milind Tambe}, journal={ArXiv}, year={2019}, volume={abs/1905.13732} }

Real-world applications often combine learning and optimization problems on graphs. For instance, our objective may be to cluster the graph in order to detect meaningful communities (or solve other common graph optimization problems such as facility location, maxcut, and so on). However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization. Standard approaches treat learning and…

## 41 Citations

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The proposed Task-Oriented Prediction Network (TOPNet), an end-to-end learning scheme that automatically integrates task-based evaluation criteria into the learning process via a task-oriented estimator and directly learns a model with respect to the task- based goal, is validated.

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Results show that this kind of one-stage end-to-end predict-then-optimize clustering method is beneficial to improve the performance of optimization results, namely the clustering results.

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This work presents a framework for graph meta-learning, and proposes an agent equipped with external memory and local action priors adapted to the underlying graphs, showing substantially improvement in one-shot performance over baseline agents.

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