- @misc{https://doi.org/10.48550/arxiv.2207.12560,
- doi = {10.48550/ARXIV.2207.12560},
- url = {https://arxiv.org/abs/2207.12560},
- author = {Gijsbers, Pieter and Bueno, Marcos L. P. and Coors,
- Stefan and LeDell, Erin and Poirier, S\'{e}bastien and Thomas, Janek
- and Bischl, Bernd and Vanschoren, Joaquin},
- keywords = {Machine Learning (cs.LG), Machine Learning
- (stat.ML), FOS: Computer and information sciences, FOS: Computer and
- information sciences},
- title = {AMLB: an AutoML Benchmark},
- publisher = {arXiv},
- year = {2022},
- copyright = {Creative Commons Attribution 4.0 International}
-
+ @article{JMLR:v25:22-0493,
+ author = {Pieter Gijsbers and Marcos L. P. Bueno and Stefan Coors and Erin LeDell and S{{\'e}}bastien Poirier and Janek Thomas and Bernd Bischl and Joaquin Vanschoren},
+ title = {AMLB: an AutoML Benchmark},
+ journal = {Journal of Machine Learning Research},
+ year = {2024},
+ volume = {25},
+ number = {101},
+ pages = {1--65},
+ url = {http://jmlr.org/papers/v25/22-0493.html}
}
- A preprint of our most recent paper is available on
- ArXiv. It includes an in-depth discussion of the different design
- decisions and its limiations, as well as a multi-faceted analysis
+ Our
+ JMLR paper introduces the benchmark. It includes an in-depth discussion of the different design
+ decisions and its limitations, as well as a multi-faceted analysis
of results from large scale comparison across 9 frameworks on more
- than 100 tasks.
-
+ than 100 tasks conducted in 2023.
Comparing different AutoML frameworks is notoriously challenging and
often done incorrectly. We introduce an open and extensible benchmark
@@ -187,16 +182,64 @@
AMLB: an AutoML Benchmark
data sets, can be easily extended with other AutoML frameworks and
tasks, and has a website with up-to-date results.
+ @article{JMLR:v25:22-0493,
+ author = {Pieter Gijsbers and Marcos L. P. Bueno and Stefan Coors and Erin LeDell and S{{\'e}}bastien Poirier and Janek Thomas and Bernd Bischl and Joaquin Vanschoren},
+ title = {AMLB: an AutoML Benchmark},
+ journal = {Journal of Machine Learning Research},
+ year = {2024},
+ volume = {25},
+ number = {101},
+ pages = {1--65},
+ url = {http://jmlr.org/papers/v25/22-0493.html}
+ }
+
+ This is the preprint of the 2024 JMLR paper, the first submission before a revision.
+ This version which reports on the experimental results obtained in 2021.
+ Please only cite this paper if you specifically refer to results reported herein,
+ and cannot use the JMLR paper for that purpose.
+