9.4k citations · 16.5k across the 107 of their papers we have counts for
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Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
Noah Hollmann, Samuel Müller, Frank Hutter
As the field of automated machine learning (AutoML) advances, it becomes increasingly important to incorporate domain knowledge into these systems. We present an approach for doing…
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
Hilde Weerts, Florian Pfisterer, Matthias Feurer +7
The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducin…
Automated Dynamic Algorithm Configuration
Steven Adriaensen, André Biedenkapp, Gresa Shala +4
The performance of an algorithm often critically depends on its parameter configuration. While a variety of automated algorithm configuration methods have been proposed to relieve…
DACBench: A Benchmark Library for Dynamic Algorithm Configuration
Theresa Eimer, André Biedenkapp, Maximilian Reimer +3
Dynamic Algorithm Configuration (DAC) aims to dynamically control a target algorithm's hyperparameters in order to improve its performance. Several theoretical and empirical result…
On the Importance of Domain Model Configuration for Automated Planning Engines
Mauro Vallati, Lukas Chrpa, Thomas L. McCluskey +1
The development of domain-independent planners within the AI Planning community is leading to "off-the-shelf" technology that can be used in a wide range of applications. Moreover,…
Neural Model-based Optimization with Right-Censored Observations
Katharina Eggensperger, Kai Haase, Philipp Müller +2
In many fields of study, we only observe lower bounds on the true response value of some experiments. When fitting a regression model to predict the distribution of the outcomes, w…