Han, Jiaqi

24 publications

AISTATS 2025 $f$-PO: Generalizing Preference Optimization with $f$-Divergence Minimization Jiaqi Han, Mingjian Jiang, Yuxuan Song, Stefano Ermon, Minkai Xu
ICLR 2025 Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models Marianne Arriola, Aaron Gokaslan, Justin T Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sekhar Sahoo, Volodymyr Kuleshov
ICCV 2025 CHORDS: Diffusion Sampling Accelerator with Multi-Core Hierarchical ODE Solvers Jiaqi Han, Haotian Ye, Puheng Li, Minkai Xu, James Zou, Stefano Ermon
ICLR 2025 CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion Joshua Kazdan, Hao Sun, Jiaqi Han, Felix Petersen, Frederick Vu, Stefano Ermon
NeurIPS 2025 GeoAda: Efficiently Finetune Geometric Diffusion Models with Equivariant Adapters Wanjia Zhao, Jiaqi Han, Siyi Gu, Mingjian Jiang, James Zou, Stefano Ermon
ICML 2025 Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints Dapeng Jiang, Xiangzhe Kong, Jiaqi Han, Mingyu Li, Rui Jiao, Wenbing Huang, Stefano Ermon, Jianzhu Ma, Yang Liu
NeurIPSW 2024 CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion Joshua Kazdan, Hao Sun, Jiaqi Han, Felix Petersen, Frederick Vu, Stefano Ermon
ICMLW 2024 Energy-Free Guidance of Geometric Diffusion Models for 3D Molecule Inverse Design Aksh Garg, Jiaqi Han, Sanjay Nagaraj, Minkai Xu
ICMLW 2024 Energy-Free Guidance of Geometric Diffusion Models for 3D Molecule Inverse Design Sanjay Nagaraj, Jiaqi Han, Aksh Garg, Minkai Xu
ICMLW 2024 Energy-Free Guidance of Geometric Diffusion Models for 3D Molecule Inverse Design Jiaqi Han, Aksh Garg, Sanjay Nagaraj, Minkai Xu
ICML 2024 Equivariant Graph Neural Operator for Modeling 3D Dynamics Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar
NeurIPS 2024 Geometric Trajectory Diffusion Models Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon
ICML 2024 Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning Yuelin Zhang, Jiacheng Cen, Jiaqi Han, Zhiqiang Zhang, Jun Zhou, Wenbing Huang
NeurIPS 2024 RelBench: A Benchmark for Deep Learning on Relational Databases Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan E. Lenssen, Yiwen Yuan, Zecheng Zhang, Xinwei He, Jure Leskovec
NeurIPSW 2024 Relational Deep Learning: Graph Representation Learning on Relational Databases Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan Eric Lenssen, Yiwen Yuan, Zecheng Zhang, Xinwei He, Jure Leskovec
NeurIPS 2024 TFG: Unified Training-Free Guidance for Diffusion Models Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Zou, Stefano Ermon
NeurIPS 2023 Crystal Structure Prediction by Joint Equivariant Diffusion Rui Jiao, Wenbing Huang, Peijia Lin, Jiaqi Han, Pin Chen, Yutong Lu, Yang Liu
ICLRW 2023 Crystal Structure Prediction by Joint Equivariant Diffusion on Lattices and Fractional Coordinates Rui Jiao, Wenbing Huang, Peijia Lin, Jiaqi Han, Pin Chen, Yutong Lu, Yang Liu
AAAI 2023 Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs Rui Jiao, Jiaqi Han, Wenbing Huang, Yu Rong, Yang Liu
ICML 2023 Subequivariant Graph Reinforcement Learning in 3D Environments Runfa Chen, Jiaqi Han, Fuchun Sun, Wenbing Huang
NeurIPS 2022 Equivariant Graph Hierarchy-Based Neural Networks Jiaqi Han, Wenbing Huang, Tingyang Xu, Yu Rong
NeurIPSW 2022 Equivariant Graph Hierarchy-Based Neural Networks Jiaqi Han, Yu Rong, Tingyang Xu, Wenbing Huang
ICLR 2022 Equivariant Graph Mechanics Networks with Constraints Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang
NeurIPS 2022 Learning Physical Dynamics with Subequivariant Graph Neural Networks Jiaqi Han, Wenbing Huang, Hengbo Ma, Jiachen Li, Josh Tenenbaum, Chuang Gan