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Poster
The Differentiable Cross-Entropy Method
Brandon Amos · Denis Yarats

Wed Jul 15 05:00 AM -- 05:45 AM & Wed Jul 15 04:00 PM -- 04:45 PM (PDT) @ Virtual #None
in Poster Session 21 »

We study the Cross-Entropy Method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant that enables us to differentiate the output of CEM with respect to the objective function's parameters. In the machine learning setting this brings CEM inside of the end-to-end learning pipeline where this has otherwise been impossible. We show applications in a synthetic energy-based structured prediction task and in non-convex continuous control. In the control setting we show how to embed optimal action sequences into a lower-dimensional space. This enables us to use policy optimization to fine-tune modeling components by differentiating through the CEM-based controller.

Keywords: Non-convex Optimization · Planning and Control · Structured Prediction · Meta-learning and Automated ML · Optimization - Non-convex
[ Slides] 

Author Information

Brandon Amos (Facebook AI Research)
Denis Yarats (New York University)

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