Yali Du

Enhancing the Robustness of Neural Collaborative Filtering Systems Under Malicious Attacks

2018 · IEEE Transactions on Multimedia

Recommendation systems have become ubiquitous in online shopping in recent decades due to their power in reducing excessive choices of customers and industries. Recent collaborative filtering methods based on the deep neural network are studied …

MHER: Model-based Hindsight Experience Replay

2021 · arXiv (Cornell University)

Solving multi-goal reinforcement learning (RL) problems with sparse rewards is generally challenging. Existing approaches have utilized goal relabeling on collected experiences to alleviate issues raised from sparse rewards. However, these methods are still limited in …

PAC Learnability of Approximate Nash Equilibrium in Bimatrix Games

2021 · arXiv (Cornell University)

Computing Nash equilibrium in bimatrix games is PPAD-hard, and many works have focused on the approximate solutions. When games are generated from a fixed unknown distribution, learning a Nash predictor via data-driven approaches can be …

Cooperative Multi-Agent Learning in a Complex World: Challenges and Solutions

2023 · Proceedings of the AAAI Conference on Artificial Intelligence

Over the past few years, artificial intelligence (AI) has achieved great success in a variety of applications, such as image classification and recommendation systems. This success has often been achieved by training machine learning models …

Causality-Aware Efficient Exploration for Cooperative Multi-Agent Reinforcement Learning

2026 · Proceedings of the AAAI Conference on Artificial Intelligence

Exploration is critical for cooperative multi agent reinforcement learning (MARL) to improve sample efficiency. However, existing intrinsic motivation based exploration strategies in MARL overlook the causal relationships among agents, global states, and rewards, suffering from …