We present a general framework for solving a large class of learning problems with non-linear functions of classification rates.

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

Given the constraint that the human's expected reward is satisfactory, how should we pick a distribution to model the human's choices?

We study the problem of stochastic optimization for deep learning in the parallel computing environment under communication constraints. A new algorithm is proposed in this setting where the communication and coordination of work among concurrent processes (local workers), is based on an elastic force which links the parameters they compute with a center variable stored by the parameter server (master). The algorithm enables the local workers to perform more exploration, i.e. the algorithm allows the local variables to fluctuate further from the center variable by reducing the amount of communication between local workers and the master. We empirically demonstrate that in the deep learning setting, due to the existence of many local optima, allowing more exploration can lead to the improved performance. We propose synchronous and asynchronous variants of the new algorithm.

In practice, this integral is approximated with averaging over a finite number of Monte Carlo samples.

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

We study optimization algorithms based on variance reduction for stochastic gradient descent (SGD). Remarkable recent progress has been made in this direction through development of algorithms like SAG, SVRG, SAGA. These algorithms have been shown to outperform SGD, both theoretically and empirically. However, asynchronous versions of these algorithms--a crucial requirement for modern large-scale applications--have not been studied.