Many auctions involve the sale of a variety of distinct assets. Examples are airport time slots,
delivery routes, network routing, and furniture. Because of complementarities or substitution …

Combinatorial optimization problems are typically tackled by the branch-and-bound
paradigm. We propose a new graph convolutional neural network model for learning branch …

The performance of many machine learning methods depends critically on hyperparameter
settings. Sophisticated Bayesian optimization methods have recently achieved considerable …

Tree search algorithms, such as branch-and-bound, are the most widely used tools for
solving combinatorial problems. These algorithms recursively partition the search space to …

Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a
previously unseen input, using machine learning techniques to build a model of the …

The identification of performance-optimizing parameter settings is an important part of the
development and application of algorithms. We describe an automatic framework for this …

State-of-the-art Mixed Integer Linear Programming (MILP) solvers combine systematic tree
search with a plethora of hard-coded heuristics, such as branching rules. While approaches …

Branch-and-bound is a widely used method in combinatorial optimization, including mixed
integer programming, structured prediction and MAP inference. While most work has been …

We present a new method for instance-specific algorithm configuration (ISAC). It is based on
the integration of the algorithm configuration system GGA and the recently proposed …