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.

Isometric projection

Abstract: Recently the problem of dimensionality reduction has received a lot of interests in many fields of information processing. We consider the case where data is sampled from a low dimensional manifold which is embedded in high dimensional Euclidean space. The most popular manifold learning algorithms include Locally Linear Embedding, ISOMAP, and Laplacian Eigenmap. However, these algorithms are nonlinear and only provide the embedding results of training samples. In this paper, we propose a novel linear dimensionality reduction algorithm, called Isometric Projection. Isometric Projection constructs a weighted data graph where the weights are discrete approximations of the geodesic distances on the data manifold. A linear subspace is then obtained by preserving the pairwise distances. In this way, Isometric Projection can be defined everywhere. Comparing to Principal Component Analysis (PCA) which is widely used in data processing, our algorithm is more capable of discovering the intrinsic geometrical structure. Specially, PCA is optimal only when the data space is linear, while our algorithm has no such assumption and therefore can handle more complex data space. Experimental results on two real life data sets illustrate the effectiveness of the proposed method.

An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models

Abstract: We consider the task of tuning hyperparameters in SVM models based on minimizing a smooth performance validation function, e.g., smoothed k-fold cross-validation error, using non-linear optimization techniques. The key computation in this approach is that of the gradient of the validation function with respect to hyperparameters. We show that for large-scale problems involving a wide choice of kernel-based models and validation functions, this computation can be very efficiently done; often within just a fraction of the training time. Empirical results show that a near-optimal set of hyperparameters can be identified by our approach with very few training rounds and gradient computations.

Allocating online advertisement space with unreliable estimates

Abstract: We study the problem of optimally allocating online advertisement space to budget-constrained advertisers. This problem was defined and studied from the perspective of worst-case online competitive analysis by Mehta et al.Our objective is to find an algorithm that takes advantage of the given estimates of the frequencies of keywords to compute a near optimal solution when the estimates are accurate, while at the same time maintaining a good worst-case competitive ratio in case the estimates are totally incorrect. This is motivated by real-world situations where search engines have stochastic information that provide reasonably accurate estimates of the frequency of search queries except in certain highly unpredictable yet economically valuable spikes in the search pattern.Our approach is a black-box approach: we assume we have access to an oracle that uses the given estimates to recommend an advertiser everytime a query arrives. We use this oracle to design an algorithm that provides two performance guarantees: the performance guarantee in the case that the oracle gives an accurate estimate, and its worst-case performance guarantee. Our algorithm can be fine tuned by adjusting a parameter α, giving a tradeoff curve between the two performance measures with the best competitive ratio for the worst-case scenario at one end of the curve and the optimal solution for the scenario where estimates are accurate at the other en.Finally, we demonstrate the applicability of our framework by applying it to two classical online problems, namely the lost cow and the ski rental problems.

Preoperative staging of renal cell carcinoma with inferior vena cava thrombus using multidetector CT and MRI: prospective study with histopathological correlation.

Abstract: Objective To evaluate the accuracy of multidetector computed tomography (CT) and magnetic resonance imaging (MRI) in staging and estimating renal carcinomas with caval thrombus. Methods Initially, 23 patients with suspected caval thrombi were admitted into this prospective study. Triphasic CT imaging was performed using a multidetector CT with a reconstructed slice thickness of 2 mm. 3D CT reconstructions were used to improve surgical planning. MRI protocol included: a transversal T1-weighted GE sequence with and without Gd-DTPA, a transversal T2-weighted respiratory-gated TSE, and a coronal T1-weighted GE sequence with Gd-DTPA and fat saturation. In addition, a multiphase 3D angiography was performed after Gd-DTPA injection. Patients were divided into 3 groups: caval thrombus below the insertion of the hepatic veins, within the intrahepatic vena cava, and intra-atrial extension. The results the tumor thrombus extension and staging results of 2 independent readers were correlated with surgical and histopathological staging. Results Of the 23 patients admitted, CT and MR scans of 14/13 patients respectively were correlated with histopathological workup. CT thrombus detection sensitivity and specificity for both readers was 0.93 and 0.8 respectively. MRI sensitivity and specificity for both readers was 1.0/0.85 and 0.75. Readers I and II evaluated the uppermost extension of the cranial tumor thrombus by both CT and MRI. CT and MR accuracy was 78% and 72%, 88% and 76% respectively. Conclusion In cases of a suspected tumor thrombus, MRI and multidetector CT imaging showed similar staging results. Consequently, these staging modalities can be used to assess the extension of the tumor thrombus.