Kamyar Azizzadenesheli's guest appearances

Kamyar Azizzadenesheli

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

TalkRL: The Reinforcement Learning Podcast

Robert Osazuwa Ness

Samuel Castro

Scott Clark

Celeste Kidd

Dennis Mortensen

Rob Walker

Sanmi Koyejo

Chelsea Finn

Dario Amodei

Jianxiong Xiao

Kate Darling

Jason Holmberg

Lotte Bransen

Christopher Berner

David Spiegelhalter

OwlTail

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 learning, starting with a general overview of the direction of the field, and though it might seem to be slowing, thats just a product of the light being shined constantly on the CV and NLP spaces. We dig into themes like the convergence of RL methodology with both robotics and control theory, as well as a few trends that Kamyar sees over the horizon, such as self-supervised learning approaches in RL. We also talk through Kamyar’s predictions for RL in 2022 and beyond. This was a fun conversation, and I encourage you to look through all the great resources that Kamyar shared on the show notes page at twimlai.com/go/560!

Today we’re joined by <a href="mailto:kaazizzad@gmail.com">Kamyar Azizzadenesheli</a>, an assistant professor at Purdue University, to close out our AI Rewind 2021 series! In this conversation, we focused on all things deep reinforcement learning, starting with a general overview of the direction of the field, and though it might seem to be slowing, thats just a product of the light being shined constantly on the CV and NLP spaces. We dig into themes like the convergence of RL methodology with both robotics and control theory, as well as a few trends that Kamyar sees over the horizon, such as self-supervised learning approaches in RL. We also talk through Kamyar’s predictions for RL in 2022 and beyond. This was a fun conversation, and I encourage you to look through all the great resources that Kamyar shared on the show notes page at <a href="twimlai.com/go/560" rel="nofollow" target="_blank">twimlai.com/go/560</a>!

Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli - #560

Dr. Kamyar Azizzadenesheli is a post-doctorate scholar at Caltech.  His research interest is mainly in the area of Machine Learning, from theory to practice, with the main focus in Reinforcement Learning.  He will be joining Purdue University as an Assistant CS Professor in Fall 2020. Featured References Efficient Exploration through Bayesian Deep Q-Networks Kamyar Azizzadenesheli, Animashree Anandkumar Surprising Negative Results for Generative Adversarial Tree Search Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Zachary C Lipton, Animashree Anandkumar Maybe a few considerations in Reinforcement Learning Research? Kamyar Azizzadenesheli  Additional References   Model-Based Reinforcement Learning for AtariLukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski   Near-optimal Regret Bounds for Reinforcement Learning Thomas Jaksch, Ronald Ortner, Peter Auer   Curious Model-Building Control Systems Jürgen Schmidhuber   Rainbow: Combining Improvements in Deep Reinforcement LearningMatteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver   Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix, Dileep George   Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis  

<a href="https://sites.google.com/view/kazizzad" rel="nofollow" target="_blank">Dr. Kamyar Azizzadenesheli</a> is a post-doctorate scholar at Caltech.  His research interest is mainly in the area of Machine Learning, from theory to practice, with the main focus in Reinforcement Learning.  He will be joining Purdue University as an Assistant CS Professor in Fall 2020. Featured References  <a href="https://arxiv.org/abs/1802.04412" rel="nofollow" target="_blank">Efficient Exploration through Bayesian Deep Q-Networks  </a>Kamyar Azizzadenesheli, Animashree Anandkumar <a href="https://arxiv.org/abs/1806.05780" rel="nofollow" target="_blank">Surprising Negative Results for Generative Adversarial Tree Search  </a>Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Zachary C Lipton, Animashree Anandkumar <a href="https://openreview.net/forum?id=B1ghkEdzo4" rel="nofollow" target="_blank">Maybe a few considerations in Reinforcement Learning Research?  </a>Kamyar Azizzadenesheli  Additional References <ul> <li> <a href="https://arxiv.org/abs/1903.00374" rel="nofollow" target="_blank">Model-Based Reinforcement Learning for Atari</a> Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski </li> <li> <a href="http://www.jmlr.org/papers/volume11/jaksch10a/jaksch10a.pdf" rel="nofollow" target="_blank">Near-optimal Regret Bounds for Reinforcement Learning</a> Thomas Jaksch, Ronald Ortner, Peter Auer </li> <li> <a href="https://ieeexplore.ieee.org/abstract/document/170605" rel="nofollow" target="_blank">Curious Model-Building Control Systems</a> Jürgen Schmidhuber </li> <li> <a href="https://arxiv.org/abs/1710.02298" rel="nofollow" target="_blank">Rainbow: Combining Improvements in Deep Reinforcement Learning</a> Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver </li> <li> <a href="https://arxiv.org/abs/1706.04317" rel="nofollow" target="_blank">Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics</a> Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix, Dileep George </li> <li> <a href="https://arxiv.org/abs/1712.01815" rel="nofollow" target="_blank">Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm</a> David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis </li> </ul>

Today we’re joined by Kamyar Azizzadenesheli, PhD student at the University of California, Irvine, who joins us to review the core elements of RL, along with a pair of his RL-related papers: “Efficient Exploration through Bayesian Deep Q-Networks” and “Sample-Efficient Deep RL with Generative Adversarial Tree Search.” To skip the Deep Reinforcement Learning primer conversation and jump to the research discussion, skip to the 34:30 mark of the episode. Show notes at https://twimlai.com/talk/177

Today we’re joined by Kamyar Azizzadenesheli, PhD student at the University of California, Irvine, who joins us to review the core elements of RL, along with a pair of his RL-related papers: “Efficient Exploration through Bayesian Deep Q-Networks” and “Sample-Efficient Deep RL with Generative Adversarial Tree Search.” To skip the Deep Reinforcement Learning primer conversation and jump to the research discussion, skip to the 34:30 mark of the episode. Show notes at <a href="https://twimlai.com/talk/177" rel="nofollow" target="_blank">https://twimlai.com/talk/177</a>