103 citations · 288 across the 38 of their papers we have counts for
6 papers · 1 filter
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…
On Structural Expressive Power of Graph Transformers
Wenhao Zhu, Tianyu Wen, Guojie Song +2
Graph Transformer has recently received wide attention in the research community with its outstanding performance, yet its structural expressive power has not been well analyzed. I…
Latent Magic: An Investigation into Adversarial Examples Crafted in the Semantic Latent Space
BoYang Zheng
Adversarial attacks against Deep Neural Networks(DNN) have been a crutial topic ever since \cite{goodfellow} purposed the vulnerability of DNNs. However, most prior works craft adv…
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…
Posterior Probability Matters: Doubly-Adaptive Calibration for Neural Predictions in Online Advertising
Penghui Wei, Weimin Zhang, Ruijie Hou +4
Predicting user response probabilities is vital for ad ranking and bidding. We hope that predictive models can produce accurate probabilistic predictions that reflect true likeliho…
MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration
Siguang Huang, Yunli Wang, Lili Mou +4
Most machine learning classifiers only concern classification accuracy, while certain applications (such as medical diagnosis, meteorological forecasting, and computation advertisi…