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.
no code implementations • 5 Nov 2022 • Wei zhang, Yanjun Han, Zhengyuan Zhou, Aaron Flores, Tsachy Weissman
In the past four years, a particularly important development in the digital advertising industry is the shift from second-price auctions to first-price auctions for online display ads.
no code implementations • 15 Jul 2021 • Hao He, Tian Zhou, Lihua Ren, Niklas Karlsson, Aaron Flores
For Verizon MediaDemand Side Platform(DSP), forecasting of ad campaign performance not only feeds key information to the optimization server to allow the system to operate on a high-performance mode, but also produces actionable insights to the advertisers.
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.
2 code implementations • 20 Feb 2021 • Yang Sun, Junwei Pan, Alex Zhang, Aaron Flores
The FmFM model's performance is also comparable to DNN models which require much more FLOPs in runtime.
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.
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.
no code implementations • 9 Jul 2020 • Yanjun Han, Zhengyuan Zhou, Aaron Flores, Erik Ordentlich, Tsachy Weissman
In this paper, we take an online learning angle and address the fundamental problem of learning to bid in repeated first-price auctions, where both the bidder's private valuations and other bidders' bids can be arbitrary.
2 code implementations • 17 Feb 2020 • Wei Deng, Junwei Pan, Tian Zhou, Deguang Kong, Aaron Flores, Guang Lin
To address the issue of significantly increased serving delay and high memory usage for ad serving in production, this paper presents \emph{DeepLight}: a framework to accelerate the CTR predictions in three aspects: 1) accelerate the model inference via explicitly searching informative feature interactions in the shallow component; 2) prune redundant layers and parameters at intra-layer and inter-layer level in the DNN component; 3) promote the sparsity of the embedding layer to preserve the most discriminant signals.
Ranked #7 on Click-Through Rate Prediction on Avazu
no code implementations • 12 Nov 2019 • Djordje Gligorijevic, Jelena Gligorijevic, Aaron Flores
Prospective display advertising poses a great challenge for large advertising platforms as the strongest predictive signals of users are not eligible to be used in the conversion prediction systems.
no code implementations • 24 Jul 2019 • Junwei Pan, Yizhi Mao, Alfonso Lobos Ruiz, Yu Sun, Aaron Flores
Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry.