14–17 Oct 2019

Schedule: Reinforcement Learning sessions

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9:0012:30 Tuesday, 15 October 2019
Location: Blenheim Room - Palace Suite
Edward Oakes (UC Berkeley Electrical Engineering & Computer Sciences), Peter Schafhalter (UC Berkeley RISELab), Kristian Hartikainen (University of Oxford)
Average rating: *****
(5.00, 5 ratings)
Edward Oakes, Peter Schafhalter, and Kristian Hartikainen take a deep dive into Ray, a new distributed execution framework for distributed AI applications developed by machine learning and systems researchers at RISELab, and explore Ray’s API and system architecture and sharing application examples, including several state-of-the-art distributed training, hyperparameter search, and RL algorithms. Read more.
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13:3017:00 Tuesday, 15 October 2019
Location: Windsor Suite
Sergey Ermolin (Amazon Web Services), Vineet Khare (Amazon Web Services)
Average rating: *....
(1.50, 4 ratings)
Sergey Ermolin and Vineet Khare provide a step-by-step overview on how to implement, train, and deploy a reinforcement learning (RL)-based recommender system with real-time multivariate optimization. They show you how leverage RL to implement a recommender system that optimizes an advertisement message that promotes adoption of merchant's services. Read more.
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9:409:55 Wednesday, 16 October 2019
Location: King's Suite
Emily Webber (Amazon Web Services)
Average rating: ****.
(4.40, 15 ratings)
If you've ever wondered if you could use AI to inform public policy, join Emily Webber as she combines classic economic methods with AI techniques to train a reinforcement learning agent on decades of randomized control trials. You'll learn about classic philosophical foundations for public policy decision making and how these can be applied to solve the problems that impact the many. Read more.
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13:4514:25 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Average rating: ****.
(4.67, 3 ratings)
Michael Friedrich and Stefanie Grunwald explore how an algorithm capable of playing Space Invaders can also improve your cloud service's automated scaling mechanism. Read more.
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16:0016:40 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Rajib Biswas (Ericsson)
Average rating: ****.
(4.00, 2 ratings)
Rajib Biswas outlines the application of AI algorithms like generative adversarial networks (GANs) to solve natural language synthesis tasks. Join in to learn how AI can accomplish complex tasks like machine translation, write poetry with style, read a novel, and answer your questions. Read more.
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9:209:30 Thursday, 17 October 2019
Location: King's Suite
Ian Massingham (Amazon Web Services)
Average rating: ****.
(4.00, 8 ratings)
Reinforcement learning is an advanced machine learning technique that makes short-term decisions while optimizing for a longer-term goal through trial and error. Ian Massingham dives into state-of-the-art techniques in deep reinforcement learning for a variety of use cases. Read more.
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11:0511:45 Thursday, 17 October 2019
Location: Windsor Suite
Rebecca Gu (Electron), Cris Lowery (Baringa Partners)
Average rating: ****.
(4.33, 3 ratings)
In a future of widespread algorithmic pricing, cooperation between algorithms is easier than ever, resulting in coordinated price rises. Rebecca Gu and Cris Lowery explore how a Q-learner algorithm can inadvertently reach a collusive outcome in a virtual marketplace, which industries are likely to be subject to greater restrictions or scrutiny, and what future digital regulation might look like. Read more.
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16:0016:40 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Paris Buttfield-Addison (Secret Lab), Tim Nugent (Lonely Coffee)
Average rating: ****.
(4.80, 5 ratings)
You're building a high-volume, expensive, robot-driven warehouse. Your robots need to get to the right place quickly, find the right item, and sort it to the right place without colliding with each other, the shelves, or people. But you don't have any robots, and you need to start writing the logic and training them. Paris Buttfield-Addison and Tim Nugent outline how to use a simulation to do it. Read more.

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