6 papers
HOB: A Holistically Optimized Bidding Strategy under Heterogeneous Bidding Environments
Qi Li, Wendong Huang, Qichen Ye +9
Optimizing a single advertising campaign across heterogeneous channels is a central challenge in industrial autobidding. Auction mechanisms vary across channels in ranking rules (p…
Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
Zhiyu Mou, Yiqin Lv, Miao Xu +9
Auto-bidding is a critical tool for advertisers to improve advertising performance. Recent progress has demonstrated that AI-Generated Bidding (AIGB), which learns a conditional ge…
VAO: Validation-Aligned Optimization for Cross-Task Generative Auto-Bidding
Yiqin Lv, Zhiyu Mou, Miao Xu +9
Generative auto-bidding has demonstrated strong performance in online advertising, yet it often suffers from data scarcity in small-scale settings with limited advertiser participa…
Large-Scale Auto-bidding with Nash Equilibrium Constraints
Zhiyu Mou, Miao Xu, Rongquan Bai +4
Auto-bidding has become a cornerstone of modern online advertising platforms, enabling many advertisers to automate bidding at scale and optimize campaign performance. However, pre…
Beyond Advertising: Mechanism Design for Platform-Wide Marketing Service "QuanZhanTui"
Ningyuan Li, Zhilin Zhang, Tianyan Long +8
On e-commerce platforms, sellers typically bid for impressions from ad traffic to promote their products. However, for most sellers, the majority of their sales come from organic t…
Permutation Equivariant Model-based Offline Reinforcement Learning for Auto-bidding
Zhiyu Mou, Miao Xu, Wei Chen +3
Reinforcement learning (RL) for auto-bidding has shifted from using simplistic offline simulators (Simulation-based RL Bidding, SRLB) to offline RL on fixed real datasets (Offline…