Grégoire Martinon
With a career rooted in the rigors of General Relativity, I specialized in building robust, ethical, and scalable AI systems. I have several years of experiences contributing to the open-source community, having led the development of MAPIE, the reference library in conformal predictions. I now work on reliable and scalable evaluation of GenAI systems, and took the lead of GLIDE, a new open-source package implementing state-of-the-art semi-supervised inference techniques.
Session
Agentic systems are booming, yet few reach production. A primary blocker is performance evaluation on unstructured outputs (typically text): LLM-as-Judge is cheap but biased while human annotations don't scale. To bridge this gap, practitioners need scalability without sacrificing statistical rigor.
This talk introduces Prediction-Powered Inference, a framework that combines a small set of human labels with a large volume of proxy judgments to produce unbiased performance measurements with formal confidence intervals. We will showcase GLIDE, a new Python open-source package that makes state-of-the-art techniques like PPI++ accessible to every developer.
Target Audience: ML and Software Engineers struggling to evaluate GenAI systems, and researchers in the field of prediction-powered inference. No background required.
Takeaways & Materials: All code and tutorials, will be shared via our open-source repository.