83 citations · 215 across the 19 of their papers we have counts for
4 papers · 1 filter
ShaplEIG: Bayesian Experimental Design for Shapley Value Estimation
David Rundel, Fabian Fumagalli, Maximilian Muschalik +2
Shapley values are a principled attribution measure widely used in interpretable machine learning, but their exact computation scales exponentially with the number of players, moti…
Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization
Thomas Nagler, Lennart Schneider, Bernd Bischl +1
Hyperparameter optimization is crucial for obtaining peak performance of machine learning models. The standard protocol evaluates various hyperparameter configurations using a resa…
Practical Transfer Learning for Bayesian Optimization
Matthias Feurer, Benjamin Letham, Frank Hutter +1
When hyperparameter optimization of a machine learning algorithm is repeated for multiple datasets it is possible to transfer knowledge to an optimization run on a new dataset. We…
OpenML Benchmarking Suites
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer +6
Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites of machine l…