68 citations · 203 across the 30 of their papers we have counts for
11 papers · 1 filter
MCNet: Monotonic Calibration Networks for Expressive Uncertainty Calibration in Online Advertising
Quanyu Dai, Jiaren Xiao, Zhaocheng Du +4
In online advertising, uncertainty calibration aims to adjust a ranking model's probability predictions to better approximate the true likelihood of an event, e.g., a click or a co…
Optimal Transport for Treatment Effect Estimation
Hao Wang, Zhichao Chen, Jiajun Fan +7
Estimating conditional average treatment effect from observational data is highly challenging due to the existence of treatment selection bias. Prevalent methods mitigate this issu…
Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy
Jiaren Xiao, Quanyu Dai, Xiao Shen +4
Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graph…
Out-of-distribution Detection with Implicit Outlier Transformation
Qizhou Wang, Junjie Ye, Feng Liu +5
Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection, enhancing detection capability via model fine-tuning with surrogate OOD data. However, surrogate data typi…
A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction
Quanyu Dai, Haoxuan Li, Peng Wu +5
Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues in a range of industrial applications. One of the m…
Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs
Jiaren Xiao, Quanyu Dai, Xiaochen Xie +2
The high cost of data labeling often results in node label shortage in real applications. To improve node classification accuracy, graph-based semi-supervised learning leverages th…
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction
Yikai Wang, Liang Zhang, Quanyu Dai +5
Improving the performance of click-through rate (CTR) prediction remains one of the core tasks in online advertising systems. With the rise of deep learning, CTR prediction models…
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs
Qimai Li, Xiaotong Zhang, Han Liu +2
Graph convolutional neural networks (GCN) have been the model of choice for graph representation learning, which is mainly due to the effective design of graph convolution that com…
Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
Quanyu Dai, Xiao-Ming Wu, Jiaren Xiao +2
This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in…
Adversarial Training Methods for Network Embedding
Quanyu Dai, Xiao Shen, Liang Zhang +2
Network Embedding is the task of learning continuous node representations for networks, which has been shown effective in a variety of tasks such as link prediction and node classi…
Adversarial Network Embedding
Quanyu Dai, Qiang Li, Jian Tang +1
Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization. Existing met…