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.

Controversial Issues in the Management of Pericardial Diseases

Abstract: The management of pericardial diseases is largely empirical because of the relative lack of randomized trials. The first published guidelines1,2 are a first attempt to organize current knowledge. At present, no specific guidelines have been issued by the American Heart Association and American College of Cardiology. After a literature review including a Medline search with the MeSH terms “pericarditis” and “pericardium,” we identified the following controversial issues related mainly to the management of pericarditis and pericardial effusion: (1) etiological search and hospitalization; (2) role of pericardiocentesis, pericardial biopsy, and pericardioscopy; (3) myopericarditis; (4) use of corticosteroids, nonsteroidal antiinflammatory drugs (NSAIDs), and colchicine; (5) management of refractory cases and long-term outcome; (6) role of pericardiectomy, pericardial window, and other interventional techniques; and (7) management of chronic idiopathic pericardial effusion. At the end of each issue, key points are summarized. Management of most cases is done by general practitioners or different healthcare specialists and does not require specific expertise; nevertheless, incessant and recurrent cases and specific forms (eg, tuberculous pericarditis, neoplastic pericardial disease, autoimmune conditions) require cooperation among specialties (eg, cardiology, infectious diseases, rheumatology, oncology). Specific interventional techniques (eg, pericardioscopy) and pericardiectomy should be performed in referral centers. ### Pericarditis Although the clinical diagnosis of pericarditis is relatively simple (Tables 1 and 2⇓),1–13 establishing the cause may be more difficult. A major controversy in “pericardiology” is the role of an extensive etiologic search and hospital admission for all patients with pericarditis or pericardial effusion.1–8 The causes of pericarditis are varied (Table 3),9 and the clinician should identify causes that require targeted therapies. The epidemiological background is essential to develop a rational cost-effective management program3,4,15,16; the approach may be different for research, when we attempt to reduce the number of “idiopathic” cases. In developed countries, idiopathic …

Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization

Abstract: We consider stochastic convex optimization with a strongly convex (but not necessarily smooth) objective. We give an algorithm which performs only gradient updates with optimal rate of convergence.

Active Learning from Crowds

Abstract: Obtaining labels can be expensive or time-consuming, but unlabeled data is often abundant and easier to obtain. Most learning tasks can be made more efficient, in terms of labeling cost, by intelligently choosing specific unlabeled instances to be labeled by an oracle. The general problem of optimally choosing these instances is known as active learning. As it is usually set in the context of supervised learning, active learning relies on a single oracle playing the role of a teacher. We focus on the multiple annotator scenario where an oracle, who knows the ground truth, no longer exists; instead, multiple labelers, with varying expertise, are available for querying. This paradigm posits new challenges to the active learning scenario. We can now ask which data sample should be labeled next and which annotator should be queried to benefit our learning model the most. In this paper, we employ a probabilistic model for learning from multiple annotators that can also learn the annotator expertise even when their expertise may not be consistently accurate across the task domain. We then focus on providing a criterion and formulation that allows us to select both a sample and the annotator/s to query the labels from.

Building Support Vector Machines with Reduced Classifier Complexity

Abstract: Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem we devise a primal method with the following properties: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily finds a set of kernel basis functions of a specified maximum size (dmax) to approximate the SVM primal cost function well; (3) it is efficient and roughly scales as O(ndmax2) where n is the number of training examples; and, (4) the number of basis functions it requires to achieve an accuracy close to the SVM accuracy is usually far less than the number of SVM support vectors.

The Yahoo! Music Dataset and KDD-Cup'11

Abstract: KDD-Cup 2011 challenged the community to identify user tastes in music by leveraging Yahoo! Music user ratings. The competition hosted two tracks, which were based on two datasets sampled from the raw data, including hundreds of millions of ratings. The underlying ratings were given to four types of musical items: tracks, albums, artists, and genres, forming a four level hierarchical taxonomy. The challenge started on March 15, 2011 and ended on June 30, 2011 attracting 2389 participants, 2100 of which were active by the end of the competition. The popularity of the challenge is related to the fact that learning a large scale recommender systems is a generic problem, highly relevant to the industry. In addition, the contest drew interest by introducing a number of scientific and technical challenges including dataset size, hierarchical structure of items, high resolution timestamps of ratings, and a non-conventional ranking-based task. This paper provides the organizers' account of the contest, including: a detailed analysis of the datasets, discussion of the contest goals and actual conduct, and lessons learned throughout the contest.