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ReproducedPapers.org: Openly Teaching and Structuring Machine Learning Reproducibility [chapter]

Burak Yildiz, Hayley Hung, Jesse H. Krijthe, Cynthia C. S. Liem, Marco Loog, Gosia Migut, Frans A. Oliehoek, Annibale Panichella, Przemysław Pawełczak, Stjepan Picek, Mathijs de Weerdt, Jan van Gemert
2021 Lecture Notes in Computer Science  
We present ReproducedPapers.org: an open online repository for teaching and structuring machine learning reproducibility.  ...  We use anonymous self-assessment surveys and obtained 144 responses.  ...  Having such a repository is well-suited for students and adds structure to reproducibility in machine learning.  ... 
doi:10.1007/978-3-030-76423-4_1 fatcat:5o3n6o62cfclnbiblfatjgm4xu

Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence [article]

Ana Lucic, Maurits Bleeker, Sami Jullien, Samarth Bhargav, Maarten de Rijke
2021 arXiv   pre-print
In the second iteration, we encouraged students to submit their group projects to the Machine Learning Reproducibility Challenge, resulting in 9 reports from our course being accepted for publication in  ...  We reflect on our experience teaching the course over two years, where one year coincided with a global pandemic, and propose guidelines for teaching FACT-AI through reproducibility in graduate-level AI  ...  ReproducedPapers.org: Openly Teach- ing and Structuring Machine Learning Reproducibility. In International Workshop on Reproducible Research in Pat- tern Recognition, 3–11.  ... 
arXiv:2111.00826v4 fatcat:sjz4gewk7zhsrjh5lg3t6ctg4q