108 citations · 138 across the 12 of their papers we have counts for
13 papers
PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning
Huandong Wang, Changzheng Gao, Yuchen Wu +3
Generating human mobility trajectories is of great importance to solve the lack of large-scale trajectory data in numerous applications, which is caused by privacy concerns. Howeve…
On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems
Xiaocong Chen, Siyu Wang, Julian McAuley +2
Reinforcement learning serves as a potent tool for modeling dynamic user interests within recommender systems, garnering increasing research attention of late. However, a significa…
Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning
Siyu Wang, Xiaocong Chen, Dietmar Jannach +1
Reinforcement learning-based recommender systems have recently gained popularity. However, the design of the reward function, on which the agent relies to optimize its recommendati…
To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods
Dawen Zhang, Shidong Pan, Thong Hoang +6
The right to be forgotten (RTBF) is motivated by the desire of people not to be perpetually disadvantaged by their past deeds. For this, data deletion needs to be deep and permanen…
Emerging Synergies in Causality and Deep Generative Models: A Survey
Guanglin Zhou, Shaoan Xie, Guang-Yuan Hao +7
In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance. Deep generative models (DGMs) have prov…
Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation
Xiaocong Chen, Siyu Wang, Lina Yao +2
Deep reinforcement learning (DRL) has been proven its efficiency in capturing users' dynamic interests in recent literature. However, training a DRL agent is challenging, because o…
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects
Guanglin Zhou, Lina Yao, Xiwei Xu +2
We regularly consider answering counterfactual questions in practice, such as "Would people with diabetes take a turn for the better had they choose another medication?". Observati…
IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation
Jing Du, Zesheng Ye, Lina Yao +2
Recent sequential recommendation models rely increasingly on consecutive short-term user-item interaction sequences to model user interests. These approaches have, however, raised…
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems
Guanglin Zhou, Chengkai Huang, Xiaocong Chen +4
The field of generating recommendations within the framework of causal inference has seen a recent surge, with recommendations being likened to treatments. This approach enhances i…
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation
Siyu Wang, Xiaocong Chen, Lina Yao +2
Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems. How…
Contrastive Conditional Neural Processes
Zesheng Ye, Lina Yao
Conditional Neural Processes~(CNPs) bridge neural networks with probabilistic inference to approximate functions of Stochastic Processes under meta-learning settings. Given a batch…
Shape-Oriented Convolution Neural Network for Point Cloud Analysis
Chaoyi Zhang, Yang Song, Lina Yao +1
Point cloud is a principal data structure adopted for 3D geometric information encoding. Unlike other conventional visual data, such as images and videos, these irregular points de…
Learning to Recommend with Multiple Cascading Behaviors
Chen Gao, Xiangnan He, Dahua Gan +7
Most existing recommender systems leverage user behavior data of one type only, such as the purchase behavior in E-commerce that is directly related to the business KPI (Key Perfor…