Abstract: 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.

Abstract: Methods and apparatus related to determining an activity of a user based on sensor readings from sensor(s), and providing, for presentation to the user via a user interface output of a computing device of the user, information that is based on the determined activity. In some implementations, the information may be provided in response to input entered by the user via a user interface input device of the computing device of the user. In some implementations, the input may be a search query and the information may be search results. In some implementations, the input may be a partial query and the information may be query suggestions.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments are directed towards generating a unified user account trustworthiness system through user account trustworthiness scores. A trusted group of user accounts may be identified for a given action by grouping a plurality of user accounts into tiers based on a trustworthiness score of each user account for the given action. The tiers and/or trustworthiness scores may be employed to classify an item, such as a message as spam or non-spam, based on input from the user accounts. The trustworthiness scores may also be employed to determine if a user account is a robot account or a human account. The trusted group for a given action may dynamically evolve over time by regrouping the user accounts based on modified trustworthiness scores. A trustworthiness score of an individual user account may be modified based on input received from the individual user account and input from other user accounts.

Abstract: 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.

Abstract: A computer-implemented method and display advertising server network for serving impression opportunities to a frequency-capped guaranteed delivery contract in a system for delivery of display advertising to a user. The method includes steps for receiving, from a computer, an event predicate and a user ID corresponding to the user, retrieving, from an index engine, a set of eligible frequency-capped contracts, wherein an eligible contract comprises at least one target predicate matching at least a portion of the event predicate, and probabilistically selecting for serving, in a computer, the booked contract having a frequency cap specification, only when the selected frequency-capped contract can be served to the user without violating the frequency cap specification.

Abstract: A computer-implemented Internet advertising method for serving impression opportunities in a system for delivery of display advertising. The likelihood that a booked contract could be served by a future forecasted user visit is calculated as a probability mass, and associated with the booked contract. The relative sizes of the probability masses of a plurality of eligible contracts is used as a selector in conjunction with a selected pseudo-random number. In exemplary embodiments, a server is configured for receiving an event predicate as a result of a user visit to a web site. Based on the received event predicate, a set of eligible contracts is assembled. Each eligible contract is assigned to exactly one interval selected from a range, the size of the interval corresponding to the probability mass of the eligible contract. The generated pseudo-random number is used for selecting an interval, which operation selects an eligible advertisement for display.

Abstract: A method for determining a price of a contract for booking advertising space in a networked environment includes receiving, via a web server, a request to book a number of impressions from available impression inventory, where each impression corresponds to the delivery of an advertisement to a browser. The method also includes assembling user samples that represent a total amount of impression inventory, where each user sample represents a number of internet users, calculating a value associated with each piece of remaining impression inventory of the total impression inventory, and evaluating the value of all remaining impression inventory before and after allocation to a contract by maximizing and equation subject to a set of constraints. The base price for the contract corresponds to the difference between the value of the inventory before and after allocation.

Abstract: A method for allocating inventory in a networked environment includes receiving a request to purchase a number of display impressions, the request including targeting parameters and a frequency constraint corresponding to a maximum number of times the advertisement can be displayed to a user. The method also includes allocating the requested number of display impressions across a set of user samples, where the number of impressions allocated to any one user sample in the set of user samples is constrained by the frequency constraint. Allocation information that defines how the impressions are allocated among the user samples is stored to a user sample database.

Abstract: A system for performing adaptive bidding to secure Internet advertising impressions in an auction. Included are systems for analyzing advertising campaign objectives, including a campaign period, a target number of impressions, a target budget. An exemplary technique defines a bidding agent for performing the adaptive bidding seeking the minimum target spending of the budget. Objective results of the campaign such as average cost per won impression, total campaign duration relative to desired campaign period, and total expenditure relative to campaign budget can be optimized based on an empirically determined forecast. Techniques for adapting bids include statistically modeling winning bids during an exploration bidding phase, performing iterations for adjustment of the bid amounts using learn-while-bid adaptive bidding, learn-then-bid adaptive bidding, and guess-double-adjust adaptive bidding.

Abstract: A method and system for allocating inventory in an Internet environment is provided. A method employed by the system may include generating samples of representatives that represent impression inventory, where each sample represents a number of users to which impressions are deliverable. An order may be received. The order may include a number of impressions to book and target audience information. A cushion of impressions needed to guarantee delivery of the number of impressions ordered may be determined. The number of impressions ordered plus the cushion may be allocated from the samples. A contract including the target audience information, the number of impressions, and the cushion may be stored to a database.