Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach
Authors: Rémi Bardenet, Arnaud Doucet, Chris Holmes
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | All experiments were conducted using the empirical Bernstein-Serfling bound of Bardenet & Maillard (2013), which revealed equivalent to the empirical Bernstein bound in (9), and much tighter in our experience with MHSUBLHD than Hoeffding s bound in (7). All MCMC runs are adaptive Metropolis (Haario et al., 2001; Andrieu & Thoms, 2008) with target acceptance 25% when the di-mension is larger than 2 and 50% else (Roberts & Rosenthal, 2001). Hyperparameters of MHSUBLHD were set to p = 2, γ = 2, and δ = 0.01. The first two were found to work well with all experiments. |
| Researcher Affiliation | Academia | R emi Bardenet REMI.BARDENET@GMAIL.COM Arnaud Doucet DOUCET@STATS.OX.AC.UK Chris Holmes CHOLMES@STATS.OX.AC.UK Department of Statistics, University of Oxford, Oxford OX1 3TG, UK |
| Pseudocode | Yes | Figure 1. The pseudocode of the MH algorithm targeting the posterior π(θ) p(x1, ..., xn|θ)p(θ). |
| Open Source Code | No | The paper does not contain any statement about making source code publicly available or providing links to a code repository. |
| Open Datasets | Yes | We consider the dataset covtype.binary1 described in (Collobert et al., 2002). 1available at http://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/binary.html |
| Dataset Splits | No | The paper states 'we pick n = 400, 000 as a training set' for the covtype dataset and mentions a 'synthetic dataset of size n = 10^7', but it does not provide explicit training, validation, and test split percentages or counts needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory specifications, or cloud instances) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions specific statistical bounds and MCMC algorithms used (e.g., 'empirical Bernstein-Serfling bound', 'adaptive Metropolis'), but it does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | Hyperparameters of MHSUBLHD were set to p = 2, γ = 2, and δ = 0.01. The first two were found to work well with all experiments. We found empirically that the algorithm is very robust to the choice of δ. |