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

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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.

Thanks to our Sponsor!

Thanks to our friends at SigOpt, an Intel Company, for their continued support of the podcast, and their sponsorship of this series!

Experimentation is critical for AI model development, but is messy and tough to get right. This is why most modelers use tools that help them track what they’ve done. But none of these tools also help them discover what to do next. This is where SigOpt can help. SigOpt combines experiment management with seamless and powerful optimization. With SigOpt, modelers design novel experiments, explore modeling problems and optimize models to meet multiple objective metrics in their iterative workflow. Modelers from Two Sigma, OpenAI, Numenta, MILA and many more apply SigOpt to make model development 8x faster and boost team productivity by 30%. And now, SigOpt is available for free forever. Sign up for an account today at sigopt.com/signup or check out our docs to see how easy it is to get running in minutes at sigopt.com/docs.

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