TY  - JOUR
AU  - Zhen Xu
AU  - Sergio Escalera
AU  - Adrien Pavao
AU  - Magali Richard
AU  - Wei-Wei Tu
AU  - Quanming Yao
AU  - Huan Zhao
AU  - Isabelle Guyon
PY  - 2022//
TI  - Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform
T2  - PATTERNS
JO  - Patterns
SP  - 100543
VL  - 3
IS  - 7
PB  - Science Direct
KW  - Machine learning
KW  - data science
KW  - benchmark platform
KW  - reproducibility
KW  - competitions
N2  - Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community driven for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench is open to everyone free of charge and allows benchmark organizers to fairly compare submissions under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating easy organization of flexible and reproducible benchmarks, such as the possibility of reusing templates of benchmarks and supplying compute resources on demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2,500 submissions. As illustrative use cases, we introduce four diverse benchmarks covering graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning.
UR  - http://dx.doi.org/10.1016/j.patter.2022.100543
N1  - HuPBA
ID  - Zhen Xu2022
ER  -