Mirrored Langevin Dynamics
Authors: Ya-Ping Hsieh, Ali Kavis, Paul Rolland, Volkan Cevher
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we report promising experimental results for LDA on real datasets. |
| Researcher Affiliation | Academia | Laboratory for Information and Inference Systems (LIONS), EPFL, Lausanne, Switzerland {ya-ping.hsieh, ali.kavis, paul.rolland, volkan.cevher}@epfl.ch |
| Pseudocode | Yes | Algorithm 1 Stochastic Mirrored Langevin Dynamics (SMLD) |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | We implement the SMLD for LDA on the Wikipedia corpus with 100,000 documents, and we compare the performance against the SGRLD [26]. |
| Dataset Splits | No | The paper does not provide specific dataset split information for training, validation, and test sets. It mentions a "test data" but no explicit validation set or overall split percentages. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions "python, np.exp" but does not specify version numbers for Python or any libraries, which is required for reproducibility. |
| Experiment Setup | Yes | We implement the deterministic MLD for sampling from an 11-dimensional Dirichlet posterior (3.6) with n1 = 10,000, n2 = n3 = 10, and n4 = n5 = = n11 = 0, which aims to capture the sparse nature of real observations in topic modeling. We set αℓ= 0.1 for all ℓ. We perform a grid search over the constant step-size for both algorithms, and we keep the best three for MLD and SGRLD. For each iteration, we build an empirical distribution by running 2,000,000 independent trials... In order to keep the comparison fair, we adopt exactly the same setting as in [26], including the model parameters, the batch-size, the Gibbs sampler steps, etc. |