Kunal Talwar

TL;DR: The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.

TL;DR: In this paper, the authors develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy, and demonstrate that they can train deep neural networks with nonconvex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.

TL;DR: It is shown that the recent notion of differential privacv, in addition to its own intrinsic virtue, can ensure that participants have limited effect on the outcome of the mechanism, and as a consequence have limited incentive to lie.

TL;DR: This work develops new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy, and demonstrates that deep neural networks can be trained with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.

TL;DR: It is argued that data-intensive computation benefits from a fine-grain resource sharing model that differs from the coarser semi-static resource allocations implemented by most existing cluster computing architectures.