Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models
Authors: Fengzhe Zhang, Laurence Illing Midgley, José Miguel Hernández-Lobato
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the DW-4, LJ-13, and alanine-dipeptide benchmarks, VT-DIS achieves effective sample sizes of approximately 80%, 35%, and 3.5%, respectively, while using only a fraction of the computational budget required by vanilla diffusion + IS or PF-ODE based IS. Our code is available at https://github.com/fz920/cov_tuned_diffusion. |
| Researcher Affiliation | Collaboration | Fengzhe Zhang EMAIL University of Cambridge Laurence I. Midgley EMAIL Ångström AI University of Cambridge José Miguel Hernández-Lobato EMAIL Ångström AI University of Cambridge |
| Pseudocode | Yes | Algorithm 1 Post-training Covariance Tuning (VT-DIS) Require: Score network sθ, initial covariances {Σϕ}, time grid {tn}N n=0, batch size M Ensure: Tuned covariances {Σϕ } |
| Open Source Code | Yes | Our code is available at https://github.com/fz920/cov_tuned_diffusion. |
| Open Datasets | Yes | DW-4 and LJ-13. We use the 107 configurations released by Klein et al. (2023). A subset of 105 configurations trains the score model and VT-DIS; the remainder forms the test set. Alanine dipeptide. We adopt the molecular dataset of Midgley et al. (2023), which provides 105 training samples and 106 test samples. |
| Dataset Splits | Yes | DW-4 and LJ-13. We use the 107 configurations released by Klein et al. (2023). A subset of 105 configurations trains the score model and VT-DIS; the remainder forms the test set. Alanine dipeptide. We adopt the molecular dataset of Midgley et al. (2023), which provides 105 training samples and 106 test samples. |
| Hardware Specification | Yes | All experiments were run on a single NVIDIA A100 GPU (80 GB); hence every GPU hour refers to wall-clock hours on that device. |
| Software Dependencies | No | The paper mentions software components like "Adam optimizer" and "cosine annealing schedule" but does not provide specific version numbers for these or other key software libraries (e.g., Python, PyTorch, CUDA). |
| Experiment Setup | Yes | GMM-2. ... Training runs for 100,000 iterations with a batch size of 1,024 for both d = 50 and d = 100. ... All models are trained for 100,000 iterations with a batch size of 512 using the Adam optimizer, a learning rate of 0.001, and a cosine annealing schedule with a minimum learning rate of 1e-6. ... VT-DIS Post-training ... Each run comprises 5,000 optimization steps with a batch size of 512 (256 for alanine dipeptide). All runs use the Adam optimizer with a learning rate of 0.01 and a cosine annealing schedule with a minimum learning rate of 1e-6. |