Oath Inc.

Yahoo! Inc.

Yahoo Assets LLC

Verizon Media Inc.

Achint Oommen Thomas

Belle L Tseng

Yi Chang

Wei Chu

Lihong Li

Martin Zinkevich

Liang Zhang

Amanda Joy Stent

Keiko Horiguchi

Anirban Dasgupta

Ricardo Alberto Baeza-Yates

Maxim Gurevich

Jeffrey Kuwano

Manjana Chandrasekharan

Chao Qin

Hua Ouyang

Makoto Yamada

Software for online active learning receives content posted to an online stream at a website. The software converts the content into an elemental representation and inputs the elemental representation into a probit model to obtain a predictive probability that the content is abusive. The software also calculates an importance weight based on the elemental representation. And the software updates the probit model using the content, the importance weight, and an acquired label if a condition is met. The condition depends on an instrumental distribution. The software removes the content from the online stream if a condition is met. The condition depends on the predictive probability, if an acquired label is unavailable.

Online active learning in user-generated content streams

In one embodiment, a set of training data consisting of inliers may be obtained. A supervised classification model may be trained using the set of training data to identify outliers. The supervised classification model may be applied to generate an anomaly score for a data point. It may be determined whether the data point is an outlier based, at least in part, upon the anomaly score.

Large-scale anomaly detection with relative density-ratio estimation

One or more techniques and/or systems are provided for audio verification. An audio signal, comprising a code for user verification, may be identified. A second audio signal is created comprising speech. The audio signal and the second audio signal may be altered to comprise a same or similar volume, pitch, amplitude, and/or speech rate. The audio signal and the second audio signal may be combined to generate a verification audio signal. The verification audio signal may be presented to a user for the user verification. Verification may be performed to determine whether the user has access to content or a service based upon user input, obtained in response to the user verification audio signal, matching the code within the user verification audio signal. In an example, the user verification may comprise verifying that the user is human.

Audio verification

Embodiments are directed towards clustering cookies for identifying unique mobile devices for associating activities over a network with a given mobile device. The cookies are clustered based on a Bayes Factor similarity model that is trained from cookie features of known mobile devices. The clusters may be used to determine the number of unique mobile devices that access a website. The clusters may also be used to provide targeted content to each unique mobile device.

Clustering cookies for identifying unique mobile devices

Growing community of inventors

Buffalo, NY, United States of America

Achint Oommen Thomas