Asynchronous Decentralized Accelerated Stochastic Gradient Descent
In this work, we introduce an asynchronous decentralized accelerated stochastic gradient descent type of method for decentralized stochastic optimization, considering communication and synchronization are the major bottlenecks. We establish $\mathcal{O}(1/\epsilon)$ (resp., $\mathcal{O}(1/\sqrt{\epsilon})$) communication complexity and $\mathcal{O}(1/\epsilon^2)$ (resp., $\mathcal{O}(1/\epsilon)$) sampling complexity for solving general convex (resp., strongly convex) problems. …

Intelligent Personal Assistant (IPA)
An automated personal assistant or an Intelligent Personal Assistant is a mobile software agent that can perform tasks, or services, on behalf of an individual based on a combination of user input, location awareness, and the ability to access information from a variety of online sources (such as weather conditions, traffic congestion, news, stock prices, user schedules, retail prices, etc.). There are two types of automated personal assistants: intelligent automated assistants (for example, Apple’s Siri and Tronton’s Cluzee), which perform concierge-type tasks (e.g., making dinner reservations, purchasing event tickets, making travel arrangements) or provide information based on voice input or commands; and smart personal agents, which automatically perform management or data-handling tasks based on online information and events often without user initiation or interaction. According to Chi-Hua Chien of Kleiner Perkins Caufield & Byers, examples of tasks that may be performed by a smart personal agent-type of automated personal assistant include schedule management (e.g., sending an alert to a dinner date that a user is running late due to traffic conditions, update schedules for both parties, and change the restaurant reservation time) and personal health management (e.g., monitoring caloric intake, heart rate and exercise regimen, then making recommendations for healthy choices). Both types of automated personal assistant technology are enabled by the combination of mobile computing devices, application programming interfaces (APIs), and the proliferation of mobile apps. However, intelligent automated assistants are designed to perform specific, one-off tasks specified by user voice instructions, while smart personal agents perform ongoing tasks (e.g., schedule management) autonomously. …

Stepwise Regression
In statistics, stepwise regression includes regression models in which the choice of predictive variables is carried out by an automatic procedure. Usually, this takes the form of a sequence of F-tests or t-tests, but other techniques are possible, such as adjusted R-square, Akaike information criterion, Bayesian information criterion, Mallows’s Cp, PRESS, or false discovery rate. The frequent practice of fitting the final selected model followed by reporting estimates and confidence intervals without adjusting them to take the model building process into account has led to calls to stop using stepwise model building altogether or to at least make sure model uncertainty is correctly reflected. …

Spatio-Temporal Deep Graph Infomax (STDGI)
Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes over time. To address these issues, we introduce Spatio-Temporal Deep Graph Infomax (STDGI)—a fully unsupervised node representation learning approach based on mutual information maximization that exploits both the temporal and spatial dynamics of the graph. Our model tackles the challenging task of node-level regression by training embeddings to maximize the mutual information between patches of the graph, at any given time step, and between features of the central nodes of patches, in the future. We demonstrate through experiments and qualitative studies that the learned representations can successfully encode relevant information about the input graph and improve the predictive performance of spatio-temporal auto-regressive forecasting models. …