Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …

While early AutoML frameworks focused on optimizing traditional ML pipelines and their
hyperparameters, a recent trend in AutoML is to focus on neural architecture search. In this …

The design of strategies for branching in Mixed Integer Programming (MIP) is guided by
cycles of parameter tuning and offline experimentation on an extremely heterogeneous …

Abstract The Algorithm Selection Problem is concerned with selecting the best algorithm to
solve a given problem on a case-by-case basis. It has become especially relevant in the last …

We investigate per-instance algorithm selection techniques for solving the Travelling
Salesman Problem (TSP), based on the two state-of-the-art inexact TSP solvers, LKH and …

Algorithm configuration (AC) is concerned with the automated search of the most suitable
parameter configuration of a parametrized algorithm. There is currently a wide variety of AC …

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a
per-instance basis in order to exploit the varying performance of algorithms over a set of …

Selecting the most appropriate algorithm to use when attempting to solve a black-box
continuous optimization problem is a challenging task. Such problems typically lack …

Cutting planes are crucial in solving mixed integer linear programs (MILP) as they facilitate
bound improvements on the optimal solution. Modern MILP solvers rely on a variety of …

Algorithm selection (AS) techniques-which involve choosing from a set of algorithms the one
expected to solve a given problem instance most efficiently-have substantially improved the …