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Maryam Fazel, University of Washington Abstract: Policy Optimization methods enjoy wide practical use in reinforcement learning (RL) for applications ranging from robotic manipulation to game-playing, partly because they are easy to implement and allow for richly parameterized policies. Yet their theoretical properties, from optimality to statistical complexity, are still not fully understood. To help develop […] |
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Aaron Roth, University of Pennsylvania Abstract: Machine learning provides us with an amazing set of tools to make predictions, but how much should we trust particular predictions? To answer this, we need a way of estimating the confidence we should have in particular predictions of black-box models. Standard tools for doing this give guarantees that […] |
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THURSDAY, OCTOBER 20 08:00 – 08:55 Breakfast 08:55 – 09:00 Welcome Andrew Kahng (UC San Diego) 09:00 – 09:30 Keynote Talk Somdeb Majumdar (Intel) 09:30 – 11:00 TILOS Research Highlights Chips: David Pan Robotics: Hao Su, Xiaolong Wang Networks: Tara Javidi Foundations: Arya Mazumdar 11:00 – 11:15 Break 11:15 – 12:30 Industry Short Talks: Big […] |
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