Hydra-MIP : Automated Algorithm Configuration and Selection for Mixed Integer Programming

By Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown. 2011

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Citations in the corpus

Learning to Branch in Mixed Integer Programming (2016, methodology): “In the context of MIP, some recent works propose ML techniques for constructing a portfolio of good parameter conﬁgura-tions for a MIP solver, and selecting the best conﬁguration for a given instance (Hutter et al. 2009; Kadioglu et al. ; Xu et al. 2011).”
Algorithm Selection for Combinatorial Search Problems: A Survey (2012, methodology): “This problem is in particular interesting as the authors of the SATzilla system decided to fundamentally change the type of Machine Learning they use in a recent publication (Xu et al., 2011).”
Efficient Parameter Importance Analysis via Ablation with Surrogates (2017, methodology): “…of our surrogate-ablation requires: i) Instance features as numerical representations of instances; these are available for many domains (Xu et al. 2008; 2011; Gebser et al. 2011; Fawcett et al. 2014) or can be generated in a domain-independent fashion (Loreggia et al. 2016); ii)…”
Towards Further Automation in AutoML (2018, methodology): “Our base strategy for portfolio building is greedy submodular function minimization (Krause and Golovin, 2014), which has already been successfully applied to combinatorial optimization (Xu et al., 2010, 2011; Seipp et al., 2015).”
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning (2020, methodology): “…done for most work mentioned above), which directly optimizes the hyperparameters of ML pipelines to add next to the portfolio in a greedy fashion (Xu et al., 2010, 2011; Seipp et al., 2015), to jointly optimize all configurations of the portfolio with global optimization (Winkelmolen et al.,…”