Hybrid Consensus Alternating Direction Method of Multipliers (H-CADMM) The present work introduces the hybrid consensus alternating direction method of multipliers (H-CADMM), a novel framework for optimization over networks which unifies existing distributed optimization approaches, including the centralized and the decentralized consensus ADMM. H-CADMM provides a flexible tool that leverages the underlying graph topology in order to achieve a desirable sweet-spot between node-to-node communication overhead and rate of convergence — thereby alleviating known limitations of both C-CADMM and D-CADMM. A rigorous analysis of the novel method establishes linear convergence rate, and also guides the choice of parameters to optimize this rate. The novel hybrid update rules of H-CADMM lend themselves to ‘in-network acceleration’ that is shown to effect considerable — and essentially ‘free-of-charge’ — performance boost over the fully decentralized ADMM. Comprehensive numerical tests validate the analysis and showcase the potential of the method in tackling efficiently, widely useful learning tasks. …

We’re done with the basics of handling data in R. Now we want to know how to make sense of it. We know what kind of data it is, we know how to look at column names, dimensions and the like. If you’re trying to add value to this data however, that very often isn’t enough, so here’s a look at using the tools available to you to start figuring out how to do what you want.

Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time, on the other hand, an answerer might face numerous new questions without being able to identify their questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions. A Survey on Expert Recommendation in Community Question Answering

Rafael Irizarry ** 2018/09/28

Propensity Score Methods for Causal Inference: an Overview

Federated AI for building AI Solutions across Multiple Agencies

Founded by Sue Gardner, the former head of the Wikimedia Foundation and Julia Angwin and Jeff Larson, journalists formerly for ProPublica, The Markup will aim to use data to help non-experts better understand everyday technologies that often go unchecked.

Are you intrigued by buzzwords Machine Learning and Deep learning but you have always found them to be ambiguous and often used interchangeably?

Two-Wing Optimization Strategy (TwoWingOS) Determining whether a given claim is supported by evidence is a fundamental NLP problem that is best modeled as Textual Entailment. However, given a large collection of text, finding evidence that could support or refute a given claim is a challenge in itself, amplified by the fact that different evidence might be needed to support or refute a claim. Nevertheless, most prior work decouples evidence identification from determining the truth value of the claim given the evidence. We propose to consider these two aspects jointly. We develop TwoWingOS (two-wing optimization strategy), a system that, while identifying appropriate evidence for a claim, also determines whether or not the claim is supported by the evidence. Given the claim, TwoWingOS attempts to identify a subset of the evidence candidates; given the predicted evidence, it then attempts to determine the truth value of the corresponding claim. We treat this challenge as coupled optimization problems, training a joint model for it. TwoWingOS offers two advantages: (i) Unlike pipeline systems, it facilitates flexible-size evidence set, and (ii) Joint training improves both the claim entailment and the evidence identification. Experiments on a benchmark dataset show state-of-the-art performance. Code: https://…/FEVER …

Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning