Haokun Chen

FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning

2024 · Proceedings of the AAAI Conference on Artificial Intelligence

Recently, foundation models have exhibited remarkable advancements in multi-modal learning. These models, equipped with millions (or billions) of parameters, typically require a substantial amount of data for finetuning. However, collecting and centralizing training data from …

Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to …