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Found 11 papers, 3 papers with code
Nonlinear Kalman Filtering with Reparametrization Gradients
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.
Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions
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.
Mid-flight Forecasting for CPA Lines in Online Advertising
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.
An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions.
$FM^2$: Field-matrixed Factorization Machines for Recommender Systems
The FmFM model's performance is also comparable to DNN models which require much more FLOPs in runtime.
Bid Shading by Win-Rate Estimation and Surplus Maximization
We found that bid shading, in general, can deliver significant value to advertisers, reducing price per impression to about 55% of the unshaded cost.
Bid Shading in The Brave New World of First-Price Auctions
Online auctions play a central role in online advertising, and are one of the main reasons for the industry's scalability and growth.
Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions
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.
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
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
Time-Aware Prospective Modeling of Users for Online Display Advertising
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.
Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising
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.
