October 11, 2018 - BayLearn 2018 Symposium
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8:00am
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Registration opens
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8:00am - 9:00am
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Coffee & Light Breakfast
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9:05am
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Welcome to BayLearn 2018!
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9:15am
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Keynote 1
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Data-Driven Dialogue Systems: Models, Algorithms, Evaluation, and Ethical Challenges
Joelle Pineau, Facebook & McGill University |
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10:00am Chair: Jerremy Holland
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Session 1
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Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
George Tucker, Dieterich Lawson, Chris J Maddison |
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Local Linear Forests: Leveraging Smoothness with Random Forests
Rina S Friedberg, Julie Tibshirani, Susan Athey, Stefan Wager |
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Deep Learning for Supercomputers
Noam M Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake Hechtman |
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10:45am - 11:10am
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Break
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11:15am
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Keynote 2
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Some representation, optimization and generalization properties of deep networks
Peter Bartlett, CS and Statistics, UC Berkeley |
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12:00pm Chair: Jean-Francois Paiement
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Session 2
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A Deep Generative Model for Semi-Supervised Classification with Noisy Labels
Maxime Langevin |
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Improved Mixed-Example Data Augmentation
Cecilia Summers |
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12:30pm - 1:50pm
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Lunch
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2:00pm
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Keynote 3
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Statistical and machine learning challenges from genetics to CRISPR gene editing
Jennifer Listgarten, Center for Computational Biology & Berkeley AI Research, UC Berkeley |
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2:45pm Chair: Isabelle Guyon
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Session 3
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Plannable Representations with Causal InfoGAN
Thanard Kurutach, Aviv Tamar |
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Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine |
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3:15pm - 3:40pm
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Break
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3:45pm Chair: David Grangier
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Keynote 4
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What's Wrong with Meta-Learning (and how we can fix it)
Sergey Levine, Berkeley AI Research Lab, UC Berkeley and Google |
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4:45pm
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Main Poster Session, Prizes, Food and Refreshments
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7:00pm
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End of the Symposium
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