Model ChangeLists: Characterizing Updates to ML Models

Author:

Eyuboglu Sabri1ORCID,Goel Karan2ORCID,Desai Arjun2ORCID,Chen Lingjiao3ORCID,Monfort Mathew4ORCID,Ré Chris2ORCID,Zou James5ORCID

Affiliation:

1. Computer Science, Stanford University, United States

2. Computer Science, Stanford University, USA

3. Stanford University, USA

4. Amazon Web Services, USA

5. Biomedical Data Science, Stanford University, USA

Funder

National Science Foundation

Publisher

ACM

Reference78 articles.

1. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.

2. Adaptive Importance Sampling: The past, the present, and the future

3. Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency. PMLR, 77–91.

4. Lingjiao Chen, Tracy Cai, Matei Zaharia, and James Zou. 2021. Did the Model Change? Efficiently Assessing Machine Learning API Shifts. arXiv preprint arXiv:2107.14203 (2021).

5. Lingjiao Chen Zhihua Jin Sabri Eyuboglu Christopher Re Matei Zaharia and James Y Zou. [n. d.]. HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions. Advances in Neural Information Processing Systems Datasets and Benchmarks ([n. d.]).

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