Csaba Szepesvari's guest appearances

Csaba Szepesvari

TalkRL: The Reinforcement Learning Podcast

Kamyar Azizzadenesheli

Scott Fujimoto

Jessica Hamrick

Samuel Castro

Ashley Hill

Michael Littman

Natasha Jaques

Rohin Shah

Robert Osazuwa Ness

Jordan Terry

Amy Zhang

Michael Dennis

Jess Whittlestone

Taylor Killian

Ben Eysenbach

OwlTail

Csaba Szepesvari is:  Head of the Foundations Team at DeepMind  Professor of Computer Science at the University of Alberta  Canada CIFAR AI Chair  Fellow at the Alberta Machine Intelligence Institute   Co-Author of the book Bandit Algorithms along with Tor Lattimore, and author of the book Algorithms for Reinforcement Learning  References   Bandit based monte-carlo planning, Levente Kocsis, Csaba Szepesvári   Bandit Algorithms, Tor Lattimore, Csaba Szepesvári   Algorithms for Reinforcement Learning, Csaba Szepesvári   The Predictron: End-To-End Learning and Planning, David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris   A Bayesian framework for reinforcement learning, Strens   Solving Rubik’s Cube with a Robot Hand ; Paper, OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, Lei Zhang   The Nonstochastic Multiarmed Bandit Problem, Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, and Robert E. Schapire   Deep Learning with Bayesian Principles, Mohammad Emtiyaz Khan   Tackling climate change with Machine Learning David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio 

<a href="https://sites.ualberta.ca/~szepesva/" rel="nofollow" target="_blank">Csaba Szepesvari</a> is: <ul> <li>Head of the Foundations Team at DeepMind </li> <li>Professor of Computer Science at the University of Alberta </li> <li>Canada CIFAR AI Chair </li> <li>Fellow at the Alberta Machine Intelligence Institute  </li> <li>Co-Author of the book <a href="https://tor-lattimore.com/downloads/book/book.pdf" rel="nofollow" target="_blank">Bandit Algorithms</a> along with Tor Lattimore, and author of the book <a href="https://sites.ualberta.ca/~szepesva/RLBook.html" rel="nofollow" target="_blank">Algorithms for Reinforcement Learning</a> </li> </ul>References <ul> <li> <a href="https://dl.acm.org/doi/10.1007/11871842_29" rel="nofollow" target="_blank">Bandit based monte-carlo planning</a>, Levente Kocsis, Csaba Szepesvári </li> <li> <a href="https://tor-lattimore.com/downloads/book/book.pdf" rel="nofollow" target="_blank">Bandit Algorithms</a>, Tor Lattimore, Csaba Szepesvári </li> <li> <a href="https://sites.ualberta.ca/~szepesva/RLBook.html" rel="nofollow" target="_blank">Algorithms for Reinforcement Learning</a>, Csaba Szepesvári </li> <li> <a href="https://arxiv.org/abs/1612.08810" rel="nofollow" target="_blank">The Predictron: End-To-End Learning and Planning</a>, David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris </li> <li> <a href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.1701" rel="nofollow" target="_blank">A Bayesian framework for reinforcement learning</a>, Strens </li> <li> <a href="https://openai.com/blog/solving-rubiks-cube/" rel="nofollow" target="_blank">Solving Rubik’s Cube with a Robot Hand</a> ; <a href="https://arxiv.org/abs/1910.07113" rel="nofollow" target="_blank">Paper</a>, OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, Lei Zhang </li> <li> <a href="https://epubs.siam.org/doi/abs/10.1137/S0097539701398375" rel="nofollow" target="_blank">The Nonstochastic Multiarmed Bandit Problem</a>, Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, and Robert E. Schapire </li> <li> <a href="https://slideslive.com/38923183/deep-learning-with-bayesian-principles" rel="nofollow" target="_blank">Deep Learning with Bayesian Principles</a>, Mohammad Emtiyaz Khan </li> <li> <a href="https://arxiv.org/abs/1906.05433" rel="nofollow" target="_blank">Tackling climate change with Machine Learning</a> David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio </li></ul>