Information-based branching schemes for binary linear mixed integer problems

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On learning and branching: a survey (2017, methodology): “Moreover, B&B trees are now fully explored in the training phase: the exploration is deeper than those proposed in Fischetti and Monaci (2012a) and Karzan et al. (2009), which focus instead on the top tree levels only.”
Branching on nonchimerical fractionalities (2012, background): “This is particularly encouraging if one considers the proof-of-concept nature of our implementation.”
Branch-and-bound algorithms: A survey of recent advances in searching, branching, and pruning (2016, unknown): “[71] demonstrate how better branching rules can be obtained for binary integer programming problems by incorporating a learning phase to identify sets of branching decisions that lead to fathomed nodes, and use the learned clauses to then choose branching variables in a restart phase of the algorith”
A Supervised Machine Learning Approach to Variable Branching in Branch-And-Bound (2014, background): “This stands in contrast with other ‘learning-based’ approaches to variable branching [12,17] that require for each problem to solve a new time-consuming ‘learning’ phase.”
Exact Combinatorial Optimization with Graph Convolutional Neural Networks (2019, unknown): “Following Karzan et al. [29], Fischetti and Monaci [17] and Khalil et al. [30] we allow cutting plane generation at the root node only, and deactivate solver restarts.”