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Enhancing Human Trajectory Prediction with Reinforcement Learning from Quantified Human Preferences

Published in The 8th Chinese Conference on Pattern Recognition and Computer Vision, 2025

We improve human trajectory prediction by introducing Reinforcement Learning from Human Feedback (RLHF) and Rejection Sampling techniques.

Recommended citation: Chenyou Fan, Kehui Tan, Yanzhao Chen, Tianqi Pang, Haiqi Jiang, Junjie Hu. Enhancing Human Trajectory Prediction with Reinforcement Learning from Quantified Human Preferences. In Chinese Conference on Pattern Recognition and Computer Vision, 2025.
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Heterogeneous Federated Learning with Scalable Server Mixture-of-Experts

Published in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2025

Proposed a novel Federated Mixture-of-Experts (Fed-MoE) framework to address the challenges of deploying large models in power-constrained environments. Designed an asymmetric FL mechanism where compact client models are aggregated into a large server-side MoE model, enabling efficient learning from heterogeneous data.

Recommended citation: Jingang Jiang*, Yanzhao Chen*, Xiangyang Liu, Haiqi Jiang, and Chenyou Fan. Heterogeneous federated learning with scalable server mixture-of-experts. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2025. Co-first authors: Jingang Jiang and Yanzhao Chen.
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Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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