We consider the problem of learning optimal reserve price in repeated auctions against non-myopic bidders, who may bidstrategically inorder togaininfuture rounds even if the single-round auctions are truthful.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Variance reduction has emerged in recent years as a strong competitor to stochastic gradient descent in non-convex problems, providing the first algorithms to improve upon the converge rate of stochastic gradient descent for finding first-order critical points. However, variance reduction techniques typically require carefully tuned learning rates and willingness to use excessively large "mega-batches" in order to achieve their improved results.

Such a better embedding is then decoded to a network by the decoder.

However,while existing models cansynthesize photorealistic images, they lack an understanding of our underlying 3D world. We present a new generative model,Visual Object Networks (VON), synthesizing natural images of objects with a disentangled 3D representation.