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

We consider the problem of variational Bayesian inference in a latent variable model where a (possibly complex) observed stochastic process is governed by the solution of a latent stochastic differential equation (SDE).

Sequential recommender systems are designed to capture users' evolving interests

In this paper we consider linearly constrained stochastic approximation problems with federated learning asaspecial case.

However, to obtain the desired effect, the step-size should be chosen sufficiently large, a task which is problem dependent andcanbedifficultinpractice.