publications

2025

1. 

   Progressive Tempering Sampler with Diffusion

   Severi Rissanen^*, RuiKang OuYang^*, Jiajun He, and 4 more authors

   In International Conference on Machine Learning, 2025

   * arXiv Bib Code

   *denotes equal contribution. Order decided by the winner of rock-paper-scissors (3:0): Severi (✋✋👊) v.s. ME (👊👊✂️).

   @inproceedings{rissanen2025progressive,
     title = {Progressive Tempering Sampler with Diffusion},
     author = {Rissanen, Severi and OuYang, RuiKang and He, Jiajun and Chen, Wenlin and Heinonen, Markus and Solin, Arno and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
     booktitle = {International Conference on Machine Learning},
     year = {2025},
     organization = {PMLR},
     star = {\* denotes equal contribution. Order decided by the winner of rock-paper-scissors (3:0): Severi (✋✋👊) v.s. ME (👊👊✂️).}
   }

2. 

   No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers

   Jiajun He^*, Yuanqi Du^*, Francisco Vargas, and 5 more authors

   In Workshop on Frontiers in Probabilistic Inference: Sampling Meets Learning @ ICLR, 2025

   arXiv Bib

   @inproceedings{he2025tricktreatpursuitschallenges,
     title = {No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers},
     author = {He, Jiajun and Du, Yuanqi and Vargas, Francisco and Zhang, Dinghuai and Padhy, Shreyas and OuYang, RuiKang and Gomes, Carla and Hernández-Lobato, José Miguel},
     booktitle = {Workshop on Frontiers in Probabilistic Inference: Sampling Meets Learning @ ICLR},
     year = {2025},
     primaryclass = {cs.LG},
     url = {https://arxiv.org/abs/2502.06685},
   }

3. 

   BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching

   RuiKang OuYang^*, Bo Qiang^*, and José Miguel Hernández-Lobato

   2025

   In Workshop on Machine Learning and the Physical Sciences @ NeurIPS 2024; and Workshop on ML4Molecules @ ELLIS 2024

   Awarded arXiv Bib Code Poster

   Oral at ELLIS workshop 2024

   @misc{ouyang2025bnemboltzmannsamplerbased,
     title = {BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching},
     author = {OuYang, RuiKang and Qiang, Bo and Hernández-Lobato, José Miguel},
     year = {2025},
     eprint = {2409.09787},
     archiveprefix = {arXiv},
     primaryclass = {cs.LG},
     url = {https://arxiv.org/abs/2409.09787},
     note = {In Workshop on Machine Learning and the Physical Sciences @ NeurIPS 2024; and Workshop on ML4Molecules @ ELLIS 2024},
   }

2024

1. 

   Energy-Based Diffusion Neural Sampler for Boltzmann Densities

   RuiKang OuYang

   In University of Cambridge: Mphil Thesis for Machine Learning and Machine Intelligence, 2024

   Bib HTML

   @inproceedings{ouyang_mphil_thesis,
     title = {Energy-Based Diffusion Neural Sampler for Boltzmann Densities},
     author = {OuYang, RuiKang},
     booktitle = {University of Cambridge: Mphil Thesis for Machine Learning and Machine Intelligence},
     year = {2024},
     primaryclass = {cs.LG},
     url = {https://www.mlmi.eng.cam.ac.uk/files/2023-2024/ouyang_energy-based_2024.pdf},
     paper = {https://www.mlmi.eng.cam.ac.uk/files/2023-2024/ouyang_energy-based_2024.pdf},
   }

2023

1. 

   L2G2G: A Scalable Local-to-Global Network Embedding with Graph Autoencoders

   RuiKang OuYang, Andrew Elliott, Stratis Limnios, and 2 more authors

   In International Conference on Complex Networks and Their Applications, 2023

   arXiv Bib Code

   @inproceedings{ouyang2023l2g2g,
     title = {L2G2G: A Scalable Local-to-Global Network Embedding with Graph Autoencoders},
     author = {OuYang, RuiKang and Elliott, Andrew and Limnios, Stratis and Cucuringu, Mihai and Reinert, Gesine},
     booktitle = {International Conference on Complex Networks and Their Applications},
     pages = {400--412},
     year = {2023},
     organization = {Springer},
   }