A5052770881- Generative Adversarial Networks and Image Synthesis
- Cell Image Analysis Techniques
- Recommender Systems and Techniques
- Digital Media Forensic Detection
- Machine Learning and Data Classification
- Advanced Bandit Algorithms Research
- Adversarial Robustness in Machine Learning
- Reinforcement Learning in Robotics
East China Normal University
2023
Modern scientific research and applications very often encounter 'fragmentary data' which brings big challenges to imputation prediction. By leveraging the structure of response patterns, we propose a unified flexible framework based on Generative Adversarial Nets (GAN) deal with fragmentary data label prediction at same time. Unlike most other generative model methods that either have no theoretical guarantee or only consider Missing Completed At Random (MCAR), proposed FragmGAN has...
Model-free RL-based recommender systems have recently received increasing research attention due to their capability handle partial feedback and long-term rewards. However, most existing has ignored a critical feature in systems: one user's on the same item at different times is random. The stochastic rewards property essentially differs from that classic RL scenarios with deterministic rewards, which makes much more challenging. In this paper, we first demonstrate simulator environment...
Modern scientific research and applications very often encounter "fragmentary data" which brings big challenges to imputation prediction. By leveraging the structure of response patterns, we propose a unified flexible framework based on Generative Adversarial Nets (GAN) deal with fragmentary data label prediction at same time. Unlike most other generative model methods that either have no theoretical guarantee or only consider Missing Completed At Random (MCAR), proposed FragmGAN has...