We devise a distributed asynchronous stochastic ǫgradient-based algorithm to enable a network of computing and communicating nodes to solve a constrained discrete-time time-varying stochastic convex optimization problem. Each node updates its own decision variable only once every discrete time step. Under some assumptions (among which, strong convexity, Lipschitz continuity of the gradient, per...

We consider stochastic, time-varying transportation networks, where the arc weights (arc travel times) are random variables with probability distribution functions that vary with time. Efficient procedures are widely available for determining least time paths in deterministic networks. In stochastic but time-invariant networks, least expected time paths can be determined by setting each random ...

Real-time information dissemination is essential for the success of key applications such as transportation management and battlefield monitoring. In these applications, relevant information should be disseminated to interested users in a timely fashion. However, it is challenging to support timely information dissemination due to the limited and even time-varying network bandwidth. Thus, a nai...

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In many applications of finance, biology and sociology, complex systems involve entities interacting with each other. These processes have the peculiarity of evolving over time and of comprising latent factors, which influence the system without being explicitly measured. In this work we present latent variable time-varying graphical lasso (LTGL), a method for multivariate time-series graphical...

t (x)2, as was to be shown. B Learning ✏ via Maximum-Likelihood In this section, we provide an overview of how ✏ can be learned from training data in a principled manner; the details can be found in [20, Section 4.3] and [6, Section 5]. Throughout this appendix, we assume that the kernel matrix is parametrized by a set of hyperparameters ✓ (e.g., ✓ = (⌫, l) for the Mátern kernel), and ✏. Let ȳ ...

In this paper, we present a new method for modeling timeevolving correlation networks, using a Mean Reversion Autoregressive Model, and apply this to stock market data. The work is motivated by the assumption that the price and return of a stock eventually regresses back towards their mean or average. This allows us to model the stock correlation time-series as an autoregressive process with a ...