Haoxian Chen

According to our database, Haoxian Chen authored at least 17 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Calibrating Non-Identifiable High-Dimensional Simulation Models: A Framework via Eligibility Set.

Yuanlu Bai, Tucker Balch, Haoxian Chen, Danial Dervovic, Henry Lam, Svitlana Vyetrenko

ACM Trans. Model. Comput. Simul., January, 2026

2025

SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluations.

Haoting Zhang, Haoxian Chen, Donglin Zhan, Hanyang Zhao, Henry Lam, Wenpin Tang, David D. Yao, Zeyu Zheng

CoRR, November, 2025

Understanding Sampler Stochasticity in Training Diffusion Models for RLHF.

Jiayuan Sheng, Hanyang Zhao, Haoxian Chen, David D. Yao, Wenpin Tang

CoRR, October, 2025

Fine-Tuning Diffusion Generative Models via Rich Preference Optimization.

Hanyang Zhao, Haoxian Chen, Yucheng Guo, Genta Indra Winata, Tingting Ou, Ziyu Huang, David D. Yao, Wenpin Tang

CoRR, March, 2025

Score as Action: Fine Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning.

Hanyang Zhao, Haoxian Chen, Ji Zhang, David D. Yao, Wenpin Tang

Proceedings of the Forty-second International Conference on Machine Learning, 2025

MallowsPO: Fine-Tune Your LLM with Preference Dispersions.

Haoxian Chen, Hanyang Zhao, Henry Lam, David D. Yao, Wenpin Tang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate.

Fengpei Li, Haoxian Chen, Jiahe Lin, Arkin Gupta, Xiaowei Tan, Gang Xu, Yuriy Nevmyvaka, Agostino Capponi, Henry Lam

CoRR, 2024

Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning.

Hanyang Zhao, Haoxian Chen, Ji Zhang, David D. Yao, Wenpin Tang

CoRR, 2024

Mallows-DPO: Fine-Tune Your LLM with Preference Dispersions.

Haoxian Chen, Hanyang Zhao, Henry Lam, David D. Yao, Wenpin Tang

CoRR, 2024

2023

Pseudo-Bayesian Optimization.

Haoxian Chen, Henry Lam

CoRR, 2023

2022

From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers.

Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamás Sarlós, Adrian Weller, Thomas Weingarten

Proceedings of the International Conference on Machine Learning, 2022

Hybrid Random Features.

Krzysztof Marcin Choromanski, Han Lin, Haoxian Chen, Arijit Sehanobish, Yuanzhe Ma, Deepali Jain, Jake Varley, Andy Zeng, Michael S. Ryoo, Valerii Likhosherstov, Dmitry Kalashnikov, Vikas Sindhwani, Adrian Weller

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Graph Kernel Attention Transformers.

Krzysztof Choromanski, Han Lin, Haoxian Chen, Jack Parker-Holder

CoRR, 2021

Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set.

Yuanlu Bai, Tucker Balch, Haoxian Chen, Danial Dervovic, Henry Lam, Svitlana Vyetrenko

CoRR, 2021

Learning Prediction Intervals for Regression: Generalization and Calibration.

Haoxian Chen, Ziyi Huang, Henry Lam, Huajie Qian, Haofeng Zhang

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Demystifying Orthogonal Monte Carlo and Beyond.

Han Lin, Haoxian Chen, Krzysztof Marcin Choromanski, Tianyi Zhang, Clement Laroche

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Constrained Reinforcement Learning via Policy Splitting.

Haoxian Chen, Henry Lam, Fengpei Li, Amirhossein Meisami

Proceedings of The 12th Asian Conference on Machine Learning, 2020