# Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families

@article{Rajendran2021StructureLI, title={Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families}, author={Goutham Rajendran and Bohdan Kivva and Ming Gao and Bryon Aragam}, journal={ArXiv}, year={2021}, volume={abs/2110.04719} }

Greedy algorithms have long been a workhorse for learning graphical models, and more broadly for learning statistical models with sparse structure. In the context of learning directed acyclic graphs, greedy algorithms are popular despite their worst-case exponential runtime. In practice, however, they are very efficient. We provide new insight into this phenomenon by studying a general greedy scorebased algorithm for learning DAGs. Unlike edge-greedy algorithms such as the popular GES and hill…

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