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Ian Osband

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Manage episode 405194899 series 2536330
Content provided by Robin Ranjit Singh Chauhan. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Robin Ranjit Singh Chauhan or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.

Ian Osband is a Research scientist at OpenAI (ex DeepMind, Stanford) working on decision making under uncertainty.

We spoke about:

- Information theory and RL

- Exploration, epistemic uncertainty and joint predictions

- Epistemic Neural Networks and scaling to LLMs

Featured References

Reinforcement Learning, Bit by Bit
Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen

From Predictions to Decisions: The Importance of Joint Predictive Distributions

Zheng Wen, Ian Osband, Chao Qin, Xiuyuan Lu, Morteza Ibrahimi, Vikranth Dwaracherla, Mohammad Asghari, Benjamin Van Roy

Epistemic Neural Networks

Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

Approximate Thompson Sampling via Epistemic Neural Networks

Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

Additional References

  continue reading

61 episoade

Artwork

Ian Osband

TalkRL: The Reinforcement Learning Podcast

85 subscribers

published

iconDistribuie
 
Manage episode 405194899 series 2536330
Content provided by Robin Ranjit Singh Chauhan. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Robin Ranjit Singh Chauhan or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.

Ian Osband is a Research scientist at OpenAI (ex DeepMind, Stanford) working on decision making under uncertainty.

We spoke about:

- Information theory and RL

- Exploration, epistemic uncertainty and joint predictions

- Epistemic Neural Networks and scaling to LLMs

Featured References

Reinforcement Learning, Bit by Bit
Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen

From Predictions to Decisions: The Importance of Joint Predictive Distributions

Zheng Wen, Ian Osband, Chao Qin, Xiuyuan Lu, Morteza Ibrahimi, Vikranth Dwaracherla, Mohammad Asghari, Benjamin Van Roy

Epistemic Neural Networks

Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

Approximate Thompson Sampling via Epistemic Neural Networks

Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

Additional References

  continue reading

61 episoade

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