Publications (6)
InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
Zhichen Zeng, Xiaolong Liu, Mengyue Hang +25
Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous informati…
AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations
Danwei Li, Zhengyu Zhang, Siyang Yuan +5
Multi-task learning (MTL) aims to enhance the performance and efficiency of machine learning models by simultaneously training them on multiple tasks. However, MTL research faces t…
Hierarchical LoRA MoE for Efficient CTR Model Scaling
Zhichen Zeng, Mengyue Hang, Xiaolong Liu +11
Deep models have driven significant advances in click-through rate (CTR) prediction. While vertical scaling via layer stacking improves model expressiveness, the layer-by-layer seq…
A Collaborative Ensemble Framework for CTR Prediction
Xiaolong Liu, Zhichen Zeng, Xiaoyi Liu +13
Recent advances in foundation models have established scaling laws that enable the development of larger models to achieve enhanced performance, motivating extensive research into…
Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces
Chaofei Yang, Lei Ding, Yiran Chen +1
Deepfake represents a category of face-swapping attacks that leverage machine learning models such as autoencoders or generative adversarial networks. Although the concept of the f…
Generative Poisoning Attack Method Against Neural Networks
Chaofei Yang, Qing Wu, Hai Li +1
Poisoning attack is identified as a severe security threat to machine learning algorithms. In many applications, for example, deep neural network (DNN) models collect public data a…