In this episode, Adam discusses Multi-objective optimization with Laurent Parmentier.

Laurent works at OVHCloud, most recently as a data scientist but previously in various software engineering roles. He published his thesis on AutoML at OVHCloud, and had previously released a paper titled TPOT-SH: A Faster Optimization Algorithm to Solve the AutoML Problem on Large Datasets.

The conversation centers around two classic papers in AutoML:

• Multi-Objective Automatic Machine Learning with AutoxgboostMC by Pfisterer et al: https://arxiv.org/abs/1908.10796
• An ADMM Based Framework for AutoML Pipeline Configuration by Sijia Liu, Parikshit Yam et al: https://arxiv.org/abs/1905.00424

Enjoy!