Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Graph summarization with bounded error

Abstract: We propose a highly compact two-part representation of a given graph G consisting of a graph summary and a set of corrections. The graph summary is an aggregate graph in which each node corresponds to a set of nodes in G, and each edge represents the edges between all pair of nodes in the two sets. On the other hand, the corrections portion specifies the list of edge-corrections that should be applied to the summary to recreate G. Our representations allow for both lossless and lossy graph compression with bounds on the introduced error. Further, in combination with the MDL principle, they yield highly intuitive coarse-level summaries of the input graph G. We develop algorithms to construct highly compressed graph representations with small sizes and guaranteed accuracy, and validate our approach through an extensive set of experiments with multiple real-life graph data sets.To the best of our knowledge, this is the first work to compute graph summaries using the MDL principle, and use the summaries (along with corrections) to compress graphs with bounded error.

Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms

Abstract: Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! Buzz, and news recommendation in general. Offline evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their "partial-label" nature. Common practice is to create a simulator which simulates the online environment for the problem at hand and then run an algorithm against this simulator. However, creating simulator itself is often difficult and modeling bias is usually unavoidably introduced. In this paper, we introduce a replay methodology for contextual bandit algorithm evaluation. Different from simulator-based approaches, our method is completely data-driven and very easy to adapt to different applications. More importantly, our method can provide provably unbiased evaluations. Our empirical results on a large-scale news article recommendation dataset collected from Yahoo! Front Page conform well with our theoretical results. Furthermore, comparisons between our offline replay and online bucket evaluation of several contextual bandit algorithms show accuracy and effectiveness of our offline evaluation method.

Prospective Study of Tnf Blockade with Infliximab in Anti-Neutrophil Cytoplasmic Antibody-Associated Systemic Vasculitis

Abstract: . Tumor necrosis factor α (TNFα) plays an important role in the pathogenesis of anti-neutrophil cytoplasmic antibody-associated systemic vasculitis. TNFα blockade is a potential therapy for these disorders. Methods: An open-label, multi-center, prospective clinical trial in two subgroups was performed. Study I examined acute disease, either first presentation or relapse (Birmingham Vasculitis Activity Score [BVAS] ≥ 10; n = 16); study II examined persistent disease (BVAS ≥ 4; n = 16). Patients received infliximab (5 mg/kg) at 0, 2, 6, and 10 wk. Concomitant therapy in study I included prednisolone and cyclophosphamide. Study II patients continued their existing treatment regimens, with prednisolone tapered according to clinical status. Results: Mean age was 52.4 yr, 53% of the patients were female, and follow-up was 16.8 mo. Twenty-eight patients (88%) achieved remission (14 per study group). BVAS decreased from 12.3 (confidence interval [CI] = 10.5 to 14.0) at entry to 0.3 (CI = 0.2 to 0.9) at wk 14 ( P P = 0.001). Mean prednisolone dose (mg/d) in study II decreased from 23.8 (CI = 15.0 to 32.5) at entry to 8.8 (CI = 5.9 to 11.7) at wk 14 ( P = 0.002). There were two deaths and seven serious infections. Relapse occurred in five patients (three in study II) after a mean of 27 wk. Conclusion: TNFα blockade with infliximab was effective at inducing remission in 88% of patients with antibody-associated systemic vasculitis and permitted reduction in steroid doses. Severe infections were seen in 21% of patients, and despite continued infliximab, 20% of initial responders experienced disease flares. Infliximab is a promising new therapy for vasculitis both as a component of initial therapy and in the management of refractory disease. These results need confirmation in larger randomized trials.

Margin based active learning

Abstract: We present a framework for margin based active learning of linear separators. We instantiate it for a few important cases, some of which have been previously considered in the literature.We analyze the effectiveness of our framework both in the realizable case and in a specific noisy setting related to the Tsybakov small noise condition.

Faces engage us: photos with faces attract more likes and comments on Instagram

Abstract: Photos are becoming prominent means of communication online. Despite photos' pervasive presence in social media and online world, we know little about how people interact and engage with their content. Understanding how photo content might signify engagement, can impact both science and design, influencing production and distribution. One common type of photo content that is shared on social media, is the photos of people. From studies of offline behavior, we know that human faces are powerful channels of non-verbal communication. In this paper, we study this behavioral phenomena online. We ask how presence of a face, it's age and gender might impact social engagement on the photo. We use a corpus of 1 million Instagram images and organize our study around two social engagement feedback factors, likes and comments. Our results show that photos with faces are 38% more likely to receive likes and 32% more likely to receive comments, even after controlling for social network reach and activity. We find, however, that the number of faces, their age and gender do not have an effect. This work presents the first results on how photos with human faces relate to engagement on large scale image sharing communities. In addition to contributing to the research around online user behavior, our findings offer a new line of future work using visual analysis.