Thursday, January 24 • 1:30pm - 1:55pm
Probabilistic Deep Ensembles for Predictive Uncertainty Estimation

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Quantifying predictive uncertainty in deep learning is a challenging and yet unsolved problem. Predictive uncertainty estimates are important to know when to trust the model's predictions, especially in real-word applications, where the train and test distributions can be very different. Bayesian neural networks are currently the state-of-the-art for estimating predictive uncertainty; however these require significant modifications to the training procedure and can be computationally expensive. I'll talk about our recent work on "Probabilistic Deep Ensembles", an alternative to Bayesian neural networks, that is simple to implement, readily parallelizable, requires very little hyperparameter tuning, and yields high quality predictive uncertainty estimates. I'll discuss experiments that show that our method produces well-calibrated uncertainty estimates and is robust to dataset shift, and also highlight how we used this method in a challenging healthcare problem.

avatar for Balaji Lakshminarayanan, DeepMind

Balaji Lakshminarayanan, DeepMind

Senior Research Scientist, DeepMind
Balaji Lakshminarayanan is a senior research scientist at Google DeepMind. He's interested in scalable probabilistic machine learning and its applications. Most recently, his research has focused on probabilistic deep learning, specifically, uncertainty estimation and deep generative... Read More →

Thursday January 24, 2019 1:30pm - 1:55pm
Grand Ballroom Hyatt Regency San Francisco, 5 Embarcadero Center, San Francisco, CA 94111, USA