Constraint Active Search for Human-in-the-Loop Optimization with Gustavo Malkomes

EPISODE 505
WATCH
Play Video
Join our list for notifications and early access to events

About this Episode

Today we continue our ICML series joined by Gustavo Malkomes, a research engineer at Intel via their recent acquisition of SigOpt. In our conversation with Gustavo, we explore his paper Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design, which focuses on a novel algorithmic solution for the iterative model search process. This new algorithm empowers teams to run experiments where they are not optimizing particular metrics but instead identifying parameter configurations that satisfy constraints in the metric space. This allows users to efficiently explore multiple metrics at once in an efficient, informed, and intelligent way that lends itself to real-world, human-in-the-loop scenarios.
Connect with Gustavo
Read More

Thanks to our sponsor SigOpt

SigOpt was born out of the desire to make experts more efficient. While co-founder Scott Clark was completing his PhD at Cornell he noticed that often the final stage of research was a domain expert tweaking what they had built via trial and error. After completing his PhD, Scott developed MOE to solve this problem, and used it to optimize machine learning models and A/B tests at Yelp. SigOpt was founded in 2014 to bring this technology to every expert in every field.

SigOpt Logo

More from TWIML

Leave a Reply

Your email address will not be published.