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..
Nonparametric Hamiltonian Monte Carlo
Authors: Carol Mak, Fabian Zaiser, Luke Ong
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper introduces the Nonparametric Hamiltonian Monte Carlo (NP-HMC) algorithm which generalises HMC to nonparametric models. We provide a correctness proof of NP-HMC, and empirically demonstrate significant performance improvements over existing approaches on several nonparametric examples. |
| Researcher Affiliation | Academia | Carol Mak 1 Fabian Zaiser 1 Luke Ong 1 1Department of Computer Science, University of Oxford, United Kingdom. Correspondence to: Carol Mak <EMAIL>. |
| Pseudocode | Yes | Figure 4. Pseudocode for Nonparametric Hamiltonian Monte Carlo |
| Open Source Code | Yes | The code for our implementation and experiments is available at https://github.com/fzaiser/nonparametric-hmc and archived as (Zaiser & Mak, 2021). |
| Open Datasets | Yes | We used this model to generate N = 200 training data points for a fixed θ = (K = 9,µ 1...K ). |
| Dataset Splits | Yes | We used this model to generate N = 200 training data points for a fixed θ = (K = 9,µ 1...K ). We computed the log pointwise predictive density (LPPD) for a test set with N = 50 data points Y = {y1,...,y N }, generated from the same θ as the training data. |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory) used for running experiments are provided in the paper. |
| Software Dependencies | No | We implemented the NP-HMC algorithm and its variants (NP-RHMC and NP-DHMC) in Python, using PyTorch (Paszke et al., 2019) for automatic differentiation. While PyTorch is mentioned, a specific version number is not provided, making it not reproducible based on the strict definition. |
| Experiment Setup | Yes | Table 1. Total variation distance from the ground truth for the geometric distribution, averaged over 10 runs. Each run: 10^3 NP-DHMC samples with 10^2 burn-in, 5 leapfrog steps of size 0.1; and 5 x 10^3 LMH, PGibbs and RMH samples. |