publications

2025

1. TMLR

   Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models

   Fengzhe Zhang, Laurence I. Midgley, and José Miguel Hernández-Lobato

   Transactions on Machine Learning Research, Oct 2025

   Journal-to-Conference certification; presented as a poster at ICML 2026.

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   Variance-Tuned Diffusion Importance Sampling (VT-DIS) improves Boltzmann sampling by tuning the noise covariance of a pretrained diffusion model. Trajectory-based importance weights correct estimation bias without costly likelihood calculations. Experiments on synthetic and molecular systems demonstrate improved sampling efficiency.

   @article{zhang2025efficient,
     title = {Efficient and Unbiased Sampling from {Boltzmann} Distributions via Variance-Tuned Diffusion Models},
     author = {Zhang, Fengzhe and Midgley, Laurence I. and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
     journal = {Transactions on Machine Learning Research},
     year = {2025},
     month = oct,
     url = {https://openreview.net/forum?id=Jq2dcMCS5R},
     note = {Journal-to-Conference certification; presented as a poster at ICML 2026.}
   }

2024

1. ML4PS

   

   Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models

   Fengzhe Zhang, J. He, L. Midgley, and 2 more authors

   In Machine Learning for Physical Sciences Workshop at NeurIPS, 2024

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   This work studies efficient, unbiased sampling of Boltzmann distributions by combining consistency models with importance sampling, reducing the number of function evaluations while preserving effective sample size on synthetic and equivariant n-body systems.

   @inproceedings{zhang2024efficient,
     title = {Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models},
     author = {Zhang, Fengzhe and He, J. and Midgley, L. and Antor{\'a}n, J. and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
     booktitle = {Machine Learning for Physical Sciences Workshop at NeurIPS},
     year = {2024},
   }