TY - GEN
T1 - Automated Machine Learning & Tuning with FLAML
AU - Wang, Chi
AU - Wu, Qingyun
AU - Liu, Xueqing
AU - Quintanilla, Luis
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/8/14
Y1 - 2022/8/14
N2 - In this tutorial, we will provide an in-depth and hands-on tutorial on Automated Machine Learning & Tuning with a fast python library FLAML. We will start with an overview of the AutoML problem and the FLAML library. In the first half of the tutorial, we will then give a hands-on tutorial on how to use FLAML to automate typical machine learning tasks in an end-to-end manner with different customization options and how to perform general tuning tasks on user-defined functions. In the second half of the tutorial, we will introduce several advanced functionalities of the library. For example, zero-shot AutoML, fair AutoML, and online AutoML. We will close the tutorial with several open problems, and challenges learned from AutoML practice.
AB - In this tutorial, we will provide an in-depth and hands-on tutorial on Automated Machine Learning & Tuning with a fast python library FLAML. We will start with an overview of the AutoML problem and the FLAML library. In the first half of the tutorial, we will then give a hands-on tutorial on how to use FLAML to automate typical machine learning tasks in an end-to-end manner with different customization options and how to perform general tuning tasks on user-defined functions. In the second half of the tutorial, we will introduce several advanced functionalities of the library. For example, zero-shot AutoML, fair AutoML, and online AutoML. We will close the tutorial with several open problems, and challenges learned from AutoML practice.
KW - automl
KW - tutorial
UR - http://www.scopus.com/inward/record.url?scp=85137141722&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137141722&partnerID=8YFLogxK
U2 - 10.1145/3534678.3542636
DO - 10.1145/3534678.3542636
M3 - Conference contribution
AN - SCOPUS:85137141722
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 4828
EP - 4829
BT - KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
T2 - 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Y2 - 14 August 2022 through 18 August 2022
ER -