Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo
Authors: Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël RICHARD
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |