Trends in Reinforcement Learning with Pablo Samuel Castro - #443

Trends in Reinforcement Learning with Pablo Samuel Castro - #443

Today we kick off our annual AI Rewind series joined by friend of the show Pablo Samuel Castro, a Staff Research Software Developer at Google Brain. Pablo joined us earlier this year for a discussion about Music & AI, and his Geometric Perspective on Reinforcement Learning, as well our RL office hours during the inaugural TWIMLfest. In today’s conversation, we explore some of the latest and greatest RL advancements coming out of the major conferences this year, broken down into a few major themes, Metrics/Representations, Understanding and Evaluating Deep Reinforcement Learning, and RL in the Real World. This was a very fun conversation, and we encourage you to check out all the great papers and other resources available on the show notes page.

Episoder(778)

Using Brain Imaging to Improve Neural Networks with Alona Fyshe - #513

Using Brain Imaging to Improve Neural Networks with Alona Fyshe - #513

Today we’re joined by Alona Fyshe, an assistant professor at the University of Alberta.  We caught up with Alona on the heels of an interesting panel discussion that she participated in, centered aro...

26 Aug 202136min

Adaptivity in Machine Learning with Samory Kpotufe - #512

Adaptivity in Machine Learning with Samory Kpotufe - #512

Today we’re joined by Samory Kpotufe, an associate professor at Columbia University and program chair of the 2021 Conference on Learning Theory (COLT).  In our conversation with Samory, we explore hi...

23 Aug 202149min

A Social Scientist’s Perspective on AI with Eric Rice - #511

A Social Scientist’s Perspective on AI with Eric Rice - #511

Today we’re joined by Eric Rice, associate professor at USC, and the co-director of the USC Center for Artificial Intelligence in Society.  Eric is a sociologist by trade, and in our conversation, we...

19 Aug 202143min

Applications of Variational Autoencoders and Bayesian Optimization with José Miguel Hernández Lobato - #510

Applications of Variational Autoencoders and Bayesian Optimization with José Miguel Hernández Lobato - #510

Today we’re joined by José Miguel Hernández-Lobato, a university lecturer in machine learning at the University of Cambridge. In our conversation with Miguel, we explore his work at the intersection o...

16 Aug 202142min

Codex, OpenAI’s Automated Code Generation API with Greg Brockman - #509

Codex, OpenAI’s Automated Code Generation API with Greg Brockman - #509

Today we’re joined by return guest Greg Brockman, co-founder and CTO of OpenAI. We had the pleasure of reconnecting with Greg on the heels of the announcement of Codex, OpenAI’s most recent release. C...

12 Aug 202147min

Spatiotemporal Data Analysis with Rose Yu - #508

Spatiotemporal Data Analysis with Rose Yu - #508

Today we’re joined by Rose Yu, an assistant professor at the Jacobs School of Engineering at UC San Diego.  Rose’s research focuses on advancing machine learning algorithms and methods for analyzing ...

9 Aug 202132min

Parallelism and Acceleration for Large Language Models with Bryan Catanzaro - #507

Parallelism and Acceleration for Large Language Models with Bryan Catanzaro - #507

Today we’re joined by Bryan Catanzaro, vice president of applied deep learning research at NVIDIA. Most folks know Bryan as one of the founders/creators of cuDNN, the accelerated library for deep neu...

5 Aug 202150min

Applying the Causal Roadmap to Optimal Dynamic Treatment Rules with Lina Montoya - #506

Applying the Causal Roadmap to Optimal Dynamic Treatment Rules with Lina Montoya - #506

Today we close out our 2021 ICML series joined by Lina Montoya, a postdoctoral researcher at UNC Chapel Hill.  In our conversation with Lina, who was an invited speaker at the Neglected Assumptions in...

2 Aug 202154min

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