An Empirical Analysis of Backward Compatibility in Machine Learning Systems-Reference-Cited by-同舟云学术

An Empirical Analysis of Backward Compatibility in Machine Learning Systems

Published:2020-07-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
language:
Short-container-title:

Author:

Srivastava Megha¹,Nushi Besmira¹,Kamar Ece¹,Shah Shital¹,Horvitz Eric¹

Affiliation:

1. Microsoft Research, Redmond, WA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394486.3403379

Reference42 articles.

1. Sean Andrist Dan Bohus Ece Kamar and Eric Horvitz. 2017. What went wrong and why? diagnosing situated interaction failures in the wild. In ICSR. Sean Andrist Dan Bohus Ece Kamar and Eric Horvitz. 2017. What went wrong and why? diagnosing situated interaction failures in the wild. In ICSR.

2. Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff

3. Jan Bosch. 2009. From software product lines to software ecosystems. In SPLC. Jan Bosch. 2009. From software product lines to software ecosystems. In SPLC.

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Class-incremental learning via prototype similarity replay and similarity-adjusted regularization;Applied Intelligence;2024-07-30

2. An empirical study of testing machine learning in the wild;ACM Transactions on Software Engineering and Methodology;2024-07-24

3. Understanding user intent modeling for conversational recommender systems: a systematic literature review;User Modeling and User-Adapted Interaction;2024-06-06

4. AI model disgorgement: Methods and choices;Proceedings of the National Academy of Sciences;2024-04-19

5. "We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning;Proceedings of the ACM on Human-Computer Interaction;2024-04-17