Guaranteed display (GD) advertising is a critical component of advertising since it provides publishers with stable revenue and enables advertisers to target specific audiences with guaranteed impressions. However, smooth pacing control for online ad delivery presents a challenge due to significant budget disparities, user arrival distribution drift, and dynamic change between supply and demand. This paper presents robust risk-constrained pacing (RCPacing) that utilizes Lagrangian dual multipliers to fine-tune probabilistic throttling through monotonic mapping functions within the percentile space of impression performance distribution. RCPacing combines distribution drift resilience and compatibility with guaranteed allocation mechanism, enabling us to provide near-optimal online services. We also show that RCPacing achieves O(sqrt(T)) dynamic regret where T is the length of the horizon. RCPacing's effectiveness is validated through offline evaluations and online A/B testing conducted on Taobao brand advertising platform.

The visual Transformer model based on self-attention has achieved better performance than convolutional neural networks in object detection tasks. However, existing visual Transformer models are typically heavy-weight to extract global features. In contrast, CNNs can extract features with fewer parameters and computational costs. To combine the advantages of convolutional processing at the local level with the advantages of the Transformer's global interaction, this paper proposes MCANet, a Hierarchical Cross-Fusion Lightweight Transformer Based on Multi-ConvHead Attention for Object Detection. To bi-directionally fuse local and global features, MCANet adds two improved transformers (MCA-Former) for global interaction and two novel feature fusion modules MCA-CSP. MCA-Former uses a novel self-attention computation method named Multi-ConvHead Attention(MCA) based on multi-scale depth-separable convolution, which reduces the computational cost by 2/3. Meanwhile, the number of model parameters is reduced to 9.49 M by using channel segmentation and multi-layer cross- fusion strategies. On the Pascal VOC and COCO datasets, the proposed model outperforms YOLOv4-Tiny in terms of AP by 2.43% and 1.8%, respectively. Additionally, MCANet is also superior to many latest lightweight object detection models. Results of various ablation experiments also verify the effectiveness of the proposed method.

YOLOX is a state-of-the-art one-stage object detection model for real-time applications that employs a decoupled head and advanced label assignment. Despite its impressive performance, YOLOX has limitations that prevent it from achieving optimal accuracy in real-time settings. To improve these limitations, we propose a new approach called re-parameterization align YOLOX (RA-YOLOX). Our approach employs a novel re-parameterization align decoupled head to align the classification and regression tasks, enhancing the learning of connection information between classification and regression. In addition, we propose a novel label assignment(LA) scheme that effectively defines positive and negative samples and precisely designs loss weight function. Our LA scheme enables the detector to focus on high-quality positive samples and filter out low-quality positive samples during training. We provide three sizes of lite models, namely RA-YOLOX-s, RA-YOLOX-tiny, and RA-YOLOX-nano, all of which outperform YOLOX models of similar size by an average precision of 2.3%, 1.5%, and 1.7%, respectively, on the MS COCO-2017 validation set, demonstrating the efficacy of our approach. Our code is available at github.com/hcmyhc/RA-YOLOX.

Online display advertising has become a vital business for large-scale E-commerce markets. As the main goal of advertisers is to reach interested customer prospects, accurate conversion prediction is essential for successful online display advertising. A particular challenge for conversion prediction is that conversions may occur long after the click events. Such delayed feedback makes it a non-trivial task to keep conversion prediction models updated and consistent with the latest customer distribution. Although several studies have been conducted to tackle the delayed feedback issue, the relationship between the early conversion and full term conversion has not been fully exploited to improve conversion prediction. In this paper, we consider conversion prediction as a multi-task learning problem by leveraging multiple conversion labels after different observation intervals. Specifically, we propose a multi-task model with an end-to-end architecture for conversion prediction. Our approach is guided by theoretical and probabilistic analysis of the early and full term conversions. Our mixture-of-experts module can integrate distinct characteristics of input features and optimize the task-specific experts. In addition, the multiple tasks are jointly learned with a regularization term to ensure the embedding consistency between tasks and prevent potential overfitting issues. In comparison with competitive benchmarks, our approach can significantly improve conversion prediction with delayed feedback and improve business performance of online display advertising.