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..
Adaptive Bayesian Sampling with Monte Carlo EM
Authors: Anirban Roychowdhury, Srinivasan Parthasarathy
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In both our synthetic experiments and a high dimensional topic modeling problem with a complex Bayesian nonparametric construction [14], our samplers match or beat the Riemannian variants in sampling efficiency and accuracy, while being close to an order of magnitude faster. We show the RMSE numbers collected from post-burn-in samples as well as per-iteration runtimes in Table 1. |
| Researcher Affiliation | Academia | Anirban Roychowdhury, Srinivasan Parthasarathy Department of Computer Science and Engineering The Ohio State University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 HMC-EM; Algorithm 2 SGNHT-EM |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use count matrices from the 20-Newsgroups and Reuters Corpus Volume 1 corpora [33]. The Australian credit dataset contains 690 datapoints of dimensionality 14, and the Heart dataset has 270 13-dimensional datapoints. |
| Dataset Splits | No | The paper mentions "We used a chronological 60 40 train-test split for both datasets" but does not specify a separate validation split for the experiments conducted in this paper. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU, GPU models, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, library versions). |
| Experiment Setup | Yes | Batch sizes were fixed to 100 for all the stochastic algorithms, along with 10 leapfrog iterations across the board. For HMC we used a fairly high learning rate of 1e 2. For SGHMC and SGNHT we used A = 10 and A = 1 respectively. For SGR-NPHMC we used A, B = 0.01. Learning rates were chosen from {1e 2, 1e 4, 1e 6}, and values of the stochastic noise terms were selected from {0.001, 0.01, 0.1, 1, 10}. Leapfrog steps were chosen from {10, 20, 30}. We used initialized S_count to 300 for HMCEM, SGHMC-EM, and SGNHT-EM, and 200 for SG-NPHMC-EM. |