36 citations · 49 across the 7 of their papers we have counts for
7 papers
Counterfactual Inference for Eliminating Sentiment Bias in Recommender Systems
Le Pan, Yuanjiang Cao, Chengkai Huang +2
Recommender Systems (RSs) aim to provide personalized recommendations for users. A newly discovered bias, known as sentiment bias, uncovers a common phenomenon within Review-based…
Causality-Aware Transformer Networks for Robotic Navigation
Ruoyu Wang, Yao Liu, Yuanjiang Cao +1
Current research in Visual Navigation reveals opportunities for improvement. First, the direct adoption of RNNs and Transformers often overlooks the specific differences between Em…
Multi-agent Traffic Prediction via Denoised Endpoint Distribution
Yao Liu, Ruoyu Wang, Yuanjiang Cao +2
The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation. Trajectory prediction at high speeds requires considering…
Reinforcement Learning for Generative AI: A Survey
Yuanjiang Cao, Quan Z. Sheng, Julian McAuley +1
Deep Generative AI has been a long-standing essential topic in the machine learning community, which can impact a number of application areas like text generation and computer visi…
Guided Image-to-Image Translation by Discriminator-Generator Communication
Yuanjiang Cao, Lina Yao, Le Pan +2
The goal of Image-to-image (I2I) translation is to transfer an image from a source domain to a target domain, which has recently drawn increasing attention. One major branch of thi…
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems
Siyu Wang, Yuanjiang Cao, Xiaocong Chen +3
Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including i…
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems
Yuanjiang Cao, Xiaocong Chen, Lina Yao +2
Adversarial attacks pose significant challenges for detecting adversarial attacks at an early stage. We propose attack-agnostic detection on reinforcement learning-based interactiv…