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. With great changes in how auctions are being organized, such as changing the second- to first-price auction type, advertisers and demand platforms are compelled to adapt to a new volatile environment. Bid shading is a known technique for preventing overpaying in auction systems that can help maintain the strategy equilibrium in first-price auctions, tackling one of its greatest drawbacks. In this study, we propose a machine learning approach of modeling optimal bid shading for non-censored online first-price ad auctions. We clearly motivate the approach and extensively evaluate it in both offline and online settings on a major demand side platform. The results demonstrate the superiority and robustness of the new approach as compared to the existing approaches across a range of performance metrics.Comment: In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM'20), October 19-23, 2020, Virtual Event, Irelan

AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling

In Click-through rate (CTR) prediction models, a user's interest is usually represented as a fixed-length vector based on her history behaviors. Recently, several methods are proposed to learn an attentive weight for each user behavior and conduct weighted sum pooling. However, these methods only manually select several fields from the target item side as the query to interact with the behaviors, neglecting the other target item fields, as well as user and context fields. Directly including all these fields in the attention may introduce noise and deteriorate the performance. In this paper, we propose a novel model named AutoAttention, which includes all item/user/context side fields as the query, and assigns a learnable weight for each field pair between behavior fields and query fields. Pruning on these field pairs via these learnable weights lead to automatic field pair selection, so as to identify and remove noisy field pairs. Though including more fields, the computation cost of AutoAttention is still low due to using a simple attention function and field pair selection. Extensive experiments on the public dataset and Tencent's production dataset demonstrate the effectiveness of the proposed approach.Comment: Accepted by ICDM 202

SNR-based adaptive acquisition method for fast Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a computational imaging technique with both high resolution and large field-of-view. However, the effective numerical aperture (NA) achievable with a typical LED panel is ambiguous and usually relies on the repeated tests of different illumination NAs. The imaging quality of each raw image usually depends on the visual assessments, which is subjective and inaccurate especially for those dark field images. Moreover, the acquisition process is really time-consuming.In this paper, we propose a SNR-based adaptive acquisition method for quantitative evaluation and adaptive collection of each raw image according to the signal-to-noise ration (SNR) value, to improve the FPM's acquisition efficiency and automatically obtain the maximum achievable NA, reducing the time of collection, storage and subsequent calculation. The widely used EPRY-FPM algorithm is applied without adding any algorithm complexity and computational burden. The performance has been demonstrated in both USAF targets and biological samples with different imaging sensors respectively, which have either Poisson or Gaussian noises model. Further combined with the sparse LEDs strategy, the number of collection images can be shorten to around 25 frames while the former needs 361 images, the reduction ratio can reach over 90%. This method will make FPM more practical and automatic, and can also be used in different configurations of FPM.Comment: 11 pages, 6 figure