Research map / Probabilistic and scientific modeling / Variational and generative sampling
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use CRHMC (constrained Riemannian HMC) instead of CHRR | uniform polytope sampling on metabolic network Recon3 · Recon3 | sampling time per effective sample: 29211 -> 31.114 | Helped |
| use CRHMC instead of CHRR on small models | uniform polytope sampling · cardiac_mit (n=220) | sampling time per effective sample: 0.0059 -> 0.0100 | Hurt |
| use CRHMC instead of CHRR and CDHR on bio and LP models | uniform polytope sampling · COBRA and NETLIB benchmarks | sampling time per effective sample: up to 4 orders of magnitude | Helped |
| use CRHMC instead of coordinate hit-and-run | uniform polytope sampling · COBRA and NETLIB benchmarks | steps per effective sample (mixing rate): up to 6 orders of magnitude | Helped |
| use CRHMC on very large instances (hypercube, simplex, Birkhoff) | CRHMC · structured polytopes up to 0.5M dimension | scalability | Helped |
Closest papers by meaning
- Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics (ICML 2016 · similarity 0.91)
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- Practical Hamiltonian Monte Carlo on Riemannian Manifolds via Relativity Theory (ICML 2024 · similarity 0.86)
- Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching (ICML 2025 · similarity 0.86)
- Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling (NeurIPS 2024 · similarity 0.85)
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