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Found 8 papers, 1 papers with code
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML
Automated machine learning (AutoML) systems commonly ensemble models post hoc to improve predictive performance, typically via greedy ensemble selection (GES).
AutoML Adoption in ML Software
Machine learning (ML) has become essential to a vast range of applications, while ML experts are in short supply.
Adoption and Effects of Software Engineering Best Practices in Machine Learning
We conducted a survey among 313 practitioners to determine the degree of adoption for these practices and to validate their perceived effects.
Software Engineering
ASlib: A Benchmark Library for Algorithm Selection
To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature.
The Configurable SAT Solver Challenge (CSSC)
It is well known that different solution strategies work well for different types of instances of hard combinatorial problems.
claspfolio 2: Advances in Algorithm Selection for Answer Set Programming
The claspfolio 2 solver framework supports various feature generators, solver selection approaches, solver portfolios, as well as solver-schedule-based pre-solving techniques.
Solver Scheduling via Answer Set Programming
Although Boolean Constraint Technology has made tremendous progress over the last decade, the efficacy of state-of-the-art solvers is known to vary considerably across different types of problem instances and is known to depend strongly on algorithm parameters.
Bayesian Optimization With Censored Response Data
Bayesian optimization (BO) aims to minimize a given blackbox function using a model that is updated whenever new evidence about the function becomes available.
