Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo
Authors: Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël RICHARD
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 16,437 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,786 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support our findings using both synthetic and real data experiments. |
| Researcher Affiliation | Academia | 1: LTCI, CNRS, T el ecom Paris Tech, Universit e Paris-Saclay, 75013, Paris, France 2: Centre de Math ematiques Appliqu ees, UMR 7641, Ecole Polytechnique, France |
| Pseudocode | Yes | We provide a pseudo-code of SGRRLD in the supplementary document. |
| Open Source Code | No | The paper states, 'We have implemented SGLD and SGRRLD in C by using the GNU Scientific Library for efficient matrix computations,' and 'We provide a pseudo-code of SGRRLD in the supplementary document,' but does not provide a concrete link or explicit statement about the availability of the C implementation code. |
| Open Datasets | Yes | compare SGRRLD against SGLD on three large movie ratings datasets, namely the Movie Lens 1Million (ML-1M), Movie Lens 10Million (ML-10M), and Movie Lens 20Million (ML-20M) (grouplens.org). |
| Dataset Splits | No | The paper states, 'We randomly select 10% of the data as the test set and use the remaining data for generating the samples,' but does not explicitly specify a separate validation split or its proportion. |
| Hardware Specification | Yes | All our experiments are conducted on a standard laptop computer with 2.5GHz Quad-core Intel Core i7 CPU, and in all settings, the two chains of SGRRLD are run in parallel. |
| Software Dependencies | No | The paper states, 'We have implemented SGLD and SGRRLD in C by using the GNU Scientific Library for efficient matrix computations,' but does not specify version numbers for C or the GNU Scientific Library. |
| Experiment Setup | Yes | In our first experiment, we set d = 1, σ2 θ = 10, σ2 x = 1, N = 1000, and the size of each minibatch B = N/10. We fix the step size to γ = 10 3. In order to ensure that both algorithms are run for a fixed computation time, we run SGLD for K = 21000 iterations where we discard the first 1000 samples as burn-in, and we run SGRRLD for K = 10500 iterations accordingly, where we discard the samples generated in the first 500 iterations as burn-in. |