Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Practical Hamiltonian Monte Carlo on Riemannian Manifolds via Relativity Theory
Authors: Kai Xu, Hong Ge
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement all samplers studied in this paper using Advanced HMC.jl (Xu et al., 2020).3 Derivative implementation in (5.3) is tested by finite differentiation. Geweke tests (Geweke, 2004; Grosse & Duvenaud, 2014) are used to validate the correctness of samplers (detailed in appendix C). Appendix D lists default hyper-parameters used across experiments. |
| Researcher Affiliation | Collaboration | 1MIT-IBM Watson AI Lab, Cambridge MA, United States 2University of Cambridge, Cambridge, United Kingdom. |
| Pseudocode | Yes | Algorithm 1 Momentum sampling via Box Muller transform |
| Open Source Code | Yes | Available at https://github.com/Turing Lang/ Advanced HMC.jl |
| Open Datasets | Yes | We use the 2-dimensional Neal s funnel (Neal, 2011), hierarchical Bayesian logistic regression (HBLR) model and a log-Gaussian Cox point process (log GCPP) model, that are previously used to benchmark HMC algorithms (Heng & Jacob, 2019; Xu et al., 2021). The HBLR problem has a dimensionality of 26, and the log GCPP problem has a dimensionality of 64. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., train/validation/test percentages or counts) for the experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'Advanced HMC.jl', 'MCMCDebugging.jl', 'Turing', 'Zygote.jl', and 'Forward Diff.jl', but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | Appendix D lists default hyper-parameters used across experiments. Table 4: Common hyper-parameters used across experiments in section 6. parameter name value comment the number of leapfrog steps 8 the number of fixed-point iterations 6 scale of identity matrix added to Hessian (λ) 1 10 2 we use H + λI to regularize the Hessian initial position distribution U( 1, 1) this follows Betancourt (2013) |