This is a request to be assigned the name “easeml” for our snap, instead of “easeml-automl”, we filed a dispute already in February 2020 but we are still waiting for the resolution.
Snap ID: wDn2qvfuqRxhWCnCcypmwSBIcc5uA8wa
Summary: Ease.ml is a declarative machine learning service platform.
Publisher
Display name: Easeml
Store username: easeml
Account ID: j5UFRP90YqesLyacPYeWxwm7bSIqiIIa
Case:
The name easeml seems to be taken already in the snap store without any snap using it. We have an already working snap and a strong claim to the name with several years of work using that name:
The website:
7 academic articles using the name:
Zhang, Ce, Wentao Wu, and Tian Li. “An overreaction to the broken machine learning abstraction: The ease. ml vision.” Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. 2017.
Karlaš, B., Liu, J., Wu, W., & Zhang, C. (2018). Ease. ml in action: towards multi-tenant declarative learning services. Proceedings of the VLDB Endowment, 11(12), 2054-2057.
Li, T., Zhong, J., Liu, J., Wu, W., & Zhang, C. (2018). Ease. ml: Towards multi-tenant resource sharing for machine learning workloads. Proceedings of the VLDB Endowment, 11(5), 607-620.
Renggli, C., Hubis, F. A., Karlaš, B., Schawinski, K., Wu, W., & Zhang, C. (2019). Ease. ml/ci and Ease. ml/meter in action: towards data management for statistical generalization. Proceedings of the VLDB Endowment, 12(12), 1962-1965.
Renggli, C., Karlaš, B., Ding, B., Liu, F., Schawinski, K., Wu, W., & Zhang, C. (2019). Continuous integration of machine learning models with ease. ml/ci: Towards a rigorous yet practical treatment. arXiv preprint arXiv:1903.00278.
Hubis, F. A., Wu, W., & Zhang, C. (2019). Ease. ml/meter: Quantitative overfitting management for human-in-the-loop ml application development. arXiv preprint arXiv:1906.00299.
Renggli, C., Hubis, F. A., Karlaš, B., Schawinski, K., Wu, W., & Zhang, C. (2019). Ease. ml/ci and Ease. ml/meter in action: towards data management for statistical generalization. Proceedings of the VLDB Endowment, 12(12), 1962-1965.
Kind Regards,
Leonel Aguilar
Data Science Service and Systems Group
ETH Zurich