Dec 14, 2019
Gradient descent and automatic differentiation provide a powerful framework for composing function-approximators and training them to optimise an objective. However, there are many learning algorithms that do not optimise a single, fixed objective — such as self-play and its generalisations in Go and StarCraft, generative adversarial networks, and adversarial training for robustness. In this talk, I will argue for a new subfield of “differentiable mechanism design”. In support, I will describe extant work from the literature under the unifying themes of meta-games and second-order information.
Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
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