Causal Models in Practice at Lyft with Sean Taylor - #486

Causal Models in Practice at Lyft with Sean Taylor - #486

Today we’re joined by Sean Taylor, Staff Data Scientist at Lyft Rideshare Labs. We cover a lot of ground with Sean, starting with his recent decision to step away from his previous role as the lab director to take a more hands-on role, and what inspired that change. We also discuss his research at Rideshare Labs, where they take a more “moonshot” approach to solving the typical problems like forecasting and planning, marketplace experimentation, and decision making, and how his statistical approach manifests itself in his work. Finally, we spend quite a bit of time exploring the role of causality in the work at rideshare labs, including how systems like the aforementioned forecasting system are designed around causal models, if driving model development is more effective using business metrics, challenges associated with hierarchical modeling, and much much more. The complete show notes for this episode can be found at twimlai.com/go/486.

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SLIDE: Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning with Beidi Chen - #356

SLIDE: Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning with Beidi Chen - #356

Beidi Chen is part of the team that developed a cheaper, algorithmic, CPU alternative to state-of-the-art GPU machines. They presented their findings at NeurIPS 2019 and have since gained a lot of att...

12 Maalis 202031min

Advancements in Machine Learning with Sergey Levine - #355

Advancements in Machine Learning with Sergey Levine - #355

Today we're joined by Sergey Levine, an Assistant Professor at UC Berkeley. We last heard from Sergey back in 2017, where we explored Deep Robotic Learning. Sergey and his lab’s recent efforts have be...

9 Maalis 202043min

Secrets of a Kaggle Grandmaster with David Odaibo - #354

Secrets of a Kaggle Grandmaster with David Odaibo - #354

Imagine spending years learning ML from the ground up, from its theoretical foundations, but still feeling like you didn’t really know how to apply it. That’s where David Odaibo found himself in 2015,...

5 Maalis 202041min

NLP for Mapping Physics Research with Matteo Chinazzi - #353

NLP for Mapping Physics Research with Matteo Chinazzi - #353

Predicting the future of science, particularly physics, is the task that Matteo Chinazzi, an associate research scientist at Northeastern University focused on in his paper Mapping the Physics Researc...

2 Maalis 202035min

Metric Elicitation and Robust Distributed Learning with Sanmi Koyejo - #352

Metric Elicitation and Robust Distributed Learning with Sanmi Koyejo - #352

The unfortunate reality is that many of the most commonly used machine learning metrics don't account for the complex trade-offs that come with real-world decision making. This is one of the challenge...

27 Helmi 202056min

High-Dimensional Robust Statistics with Ilias Diakonikolas - #351

High-Dimensional Robust Statistics with Ilias Diakonikolas - #351

Today we’re joined by Ilias Diakonikolas, faculty in the CS department at the University of Wisconsin-Madison, and author of the paper Distribution-Independent PAC Learning of Halfspaces with Massart ...

24 Helmi 202036min

How AI Predicted the Coronavirus Outbreak with Kamran Khan - #350

How AI Predicted the Coronavirus Outbreak with Kamran Khan - #350

Today we’re joined by Kamran Khan, founder & CEO of BlueDot, and professor of medicine and public health at the University of Toronto. BlueDot has been the recipient of a lot of attention for being th...

19 Helmi 202051min

Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349

Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349

Today we’re joined by Emmanuel Ameisen, machine learning engineer at Stripe, and author of the recently published book “Building Machine Learning Powered Applications; Going from Idea to Product.” In ...

17 Helmi 202042min

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