Advertising Science

We combine our immense trove of data with tools from large-scale machine learning, statistical modeling, feedback control, and more to develop the high-performing and robust algorithms that drive impression allocation and ad serving. including machine learning, statistics, operations research, economics, mathematics, control theory, etc. Some representative areas of work are: click and conversion prediction, viewability and video completion prediction, incremental reach optimization, campaign budget spend pacing, bidding strategies, supply path optimization, app marketing optimization, and optimization and performance measurement on internet traffic with privacy-driven restricted user traceability.

Publications

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January 1, 2019
AdKDD

Learning from Multi-User Activity Trails for B2B Ad Targeting

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January 1, 2019
Companion Proceedings of The 2019 World Wide Web Conference

Inferring Advertiser Sentiment in Online Articles Using Wikipedia Footnotes

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January 1, 2019
CIKM

Feature Enhancement via User Similarities Networks for Improved Click Prediction in Yahoo Gemini Native

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January 1, 2019
CIKM

Soft Frequency Capping for Improved Ad Click Prediction in Yahoo Gemini Native

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January 1, 2019
American Control Conference 2019

Identification of Seasonality in Internet Traffic to Support Control of Online Advertising

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January 1, 2019
CDC - 58th IEEE Conference on Decision and Control

Adaptive Optimization and Control in Online Advertising