Nonlinear Kalman Filtering with Reparametrization Gradients

1 code implementation • 8 Mar 2023 • San Gultekin, Brendan Kitts, Aaron Flores, John Paisley

The widely used parametric approximation is based on a jointly Gaussian assumption of the state-space model, which is in turn equivalent to minimizing an approximation to the Kullback-Leibler divergence.

An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions

no code implementations • 12 Jul 2021 • Tian Zhou, Hao He, Shengjun Pan, Niklas Karlsson, Bharatbhushan Shetty, Brendan Kitts, Djordje Gligorijevic, San Gultekin, Tingyu Mao, Junwei Pan, Jianlong Zhang, Aaron Flores

Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions.

Bid Shading by Win-Rate Estimation and Surplus Maximization

no code implementations • 19 Sep 2020 • Shengjun Pan, Brendan Kitts, Tian Zhou, Hao He, Bharatbhushan Shetty, Aaron Flores, Djordje Gligorijevic, Junwei Pan, Tingyu Mao, San Gultekin, Jianlong Zhang

We found that bid shading, in general, can deliver significant value to advertisers, reducing price per impression to about 55% of the unshaded cost.

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Bid Shading in The Brave New World of First-Price Auctions

no code implementations • 2 Sep 2020 • Djordje Gligorijevic, Tian Zhou, Bharatbhushan Shetty, Brendan Kitts, Shengjun Pan, Junwei Pan, Aaron Flores

Online auctions play a central role in online advertising, and are one of the main reasons for the industry's scalability and growth.