Backprop through the Void: Optimizing Control Variates for Black-Box Gradient Estimation.

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• Geoff Roeder (University of Toronto)
• Monday 27 November 2017, 11:00-12:00
• CBL Seminar Room.

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Gradient-based optimization is the foundation of deep learning and reinforcement learning. Even when the mechanism being optimized is unknown or not differentiable, optimization using high-variance or biased gradient estimates is still often the best strategy. We introduce a general framework for learning low-variance, unbiased gradient estimators for black-box functions of random variables. Our method uses gradients of a neural network trained jointly with model parameters or policies, and is applicable in both discrete and continuous settings. We demonstrate this framework for training discrete latent-variable models. We also give an unbiased, action-conditional extension of the advantage actor-critic reinforcement learning algorithm.

This talk is part of the Machine Learning @ CUED series.

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