11 citations · 18 across the 4 of their papers we have counts for
4 papers
Adversarial Constrained Bidding via Minimax Regret Optimization with Causality-Aware Reinforcement Learning
Haozhe Wang, Chao Du, Panyan Fang +3
The proliferation of the Internet has led to the emergence of online advertising, driven by the mechanics of online auctions. In these repeated auctions, software agents participat…
Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking
Shanlei Mu, Penghui Wei, Wayne Xin Zhao +3
Multi-scenario ad ranking aims at leveraging the data from multiple domains or channels for training a unified ranking model to improve the performance at each individual scenario.…
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning
Haozhe Wang, Chao Du, Panyan Fang +4
Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems. Advertisers employ bidding strategies in RTB to optimize their advertising effects subject t…
MCMF: Multi-Constraints With Merging Features Bid Optimization in Online Display Advertising
Xiao Wang, Shaoguo Liu, Yidong Jia +4
In the Real-Time Bidding (RTB), advertisers are increasingly relying on bid optimization to gain more conversions (i.e trade or arrival). Currently, the efficiency of bid optimizat…