We operationalize these measures on two real world datasets using human surveys on the Amazon Mechanical Turk (AMT) platform, demonstrating that we capture important properties of procedurally fair decision making. Some examples include data simulators that are widely used in engineering and scientific research, generative adversarial networks (GANs) for image synthesis, and hot-off-the-press approximate inference techniques relying on implicit distributions. Experimental results show that variational continual learning outperforms state-of-the-art continual learning methods on a variety of tasks, avoiding catastrophic forgetting in a fully automatic way.We provide fast submodular mechanisms to optimize the tradeoff between procedural fairness and prediction accuracy. The majority of existing approaches to learning implicit models rely on approximating the intractable distribution or optimisation objective for gradient- based optimisation, which is liable to produce inaccurate updates and thus poor models. Vitchyr Pong, Shixiang Gu, Murtaza Dalal, and Sergey Levine.Abstract: We present an in-depth examination of the effectiveness of radial basis function kernel (beyond Gaussian) estimators based on orthogonal random feature maps.
All videos are hosted by 3rd party websites and we have no control over their contents.That is, we examine methods of learning the value distribution instead of the value function.We give results that close a number of gaps between the theoretical and algorithmic results given by Bellemare, Dabney, and Munos (2017).Our results enable practitioners more generally to estimate the benefits from applying orthogonal transforms. Traditionally, reinforcement learning algorithms average over this randomness to estimate the value function.