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.

Avsnitt(779)

100x Improvements in Deep Learning Performance with Sparsity, w/ Subutai Ahmad - #562

100x Improvements in Deep Learning Performance with Sparsity, w/ Subutai Ahmad - #562

Today we’re joined by Subutai Ahmad, VP of research at Numenta. While we’ve had numerous conversations about the biological inspirations of deep learning models with folks working at the intersection ...

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Scaling BERT and GPT for Financial Services with Jennifer Glore - #561

Scaling BERT and GPT for Financial Services with Jennifer Glore - #561

Today we’re joined by Jennifer Glore, VP of customer engineering at SambaNova Systems. In our conversation with Jennifer, we discuss how, and why, Sambanova, who is primarily focused on building hardw...

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Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli - #560

Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli - #560

Today we’re joined by Kamyar Azizzadenesheli, an assistant professor at Purdue University, to close out our AI Rewind 2021 series! In this conversation, we focused on all things deep reinforcement lea...

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Deep Reinforcement Learning at the Edge of the Statistical Precipice with Rishabh Agarwal - #559

Deep Reinforcement Learning at the Edge of the Statistical Precipice with Rishabh Agarwal - #559

Today we’re joined by Rishabh Agarwal, a research scientist at Google Brain in Montreal. In our conversation with Rishabh, we discuss his recent paper Deep Reinforcement Learning at the Edge of the St...

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Designing New Energy Materials with Machine Learning with Rafael Gomez-Bombarelli - #558

Designing New Energy Materials with Machine Learning with Rafael Gomez-Bombarelli - #558

Today we’re joined by Rafael Gomez-Bombarelli, an assistant professor in the department of material science and engineering at MIT. In our conversation with Rafa, we explore his goal of ​​fusing machi...

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Differentiable Programming for Oceanography with Patrick Heimbach - #557

Differentiable Programming for Oceanography with Patrick Heimbach - #557

Today we’re joined by Patrick Heimbach, a professor at the University of Texas working at the intersection of ML and oceanography. In our conversation with Patrick, we explore some of the challenges o...

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Trends in Machine Learning & Deep Learning with Zachary Lipton - #556

Trends in Machine Learning & Deep Learning with Zachary Lipton - #556

Today we continue our AI Rewind 2021 series joined by a friend of the show, assistant professor at Carnegie Mellon University, and AI Rewind veteran, Zack Lipton! In our conversation with Zack, we tou...

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Solving the Cocktail Party Problem with Machine Learning, w/ ‪Jonathan Le Roux - #555

Solving the Cocktail Party Problem with Machine Learning, w/ ‪Jonathan Le Roux - #555

Today we’re joined by Jonathan Le Roux, a senior principal research scientist at Mitsubishi Electric Research Laboratories (MERL). At MERL, Jonathan and his team are focused on using machine learning ...

24 Jan 202235min

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