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..
Topic Modeling via Full Dependence Mixtures
Authors: Dan Fisher, Mark Kozdoba, Shie Mannor
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In particular, we evaluate the approach on three large datasets, Neur IPS papers, a Twitter corpus, and full English Wikipedia, with a large number of topics, and show that the approach performs comparably or better than the the standard benchmarks. |
| Researcher Affiliation | Collaboration | 1Technion, Israel Institute of Technology 2NVIDIA Research. |
| Pseudocode | Yes | Algorithm 1 Computation of c M; Algorithm 2 FDM Optimization |
| Open Source Code | Yes | A reference implementation of the algorithm is available at https://github.com/fisherd3/fdm. |
| Open Datasets | Yes | We evaluate the FDM algorithm on ... three real world datasets: the Neur IPS full papers corpus, a very large (20 million tweets) Twitter dataset that was collected via the Twitter API and the full English Wikipedia. For the semi-synthetic dataset the topic quality was measured by comparison to the ground truth topics, while for the real datasets coherence and log-likelihhod on a hold-out set was measured. ... (Neur IPSPapers Corpus, 2016)... The tweets were collected via the Tweeter API... We use the full English Wikipedia corpus, as archived on 04/2020. |
| Dataset Splits | No | The paper specifies that '20% of the documents were taken at random as a hold-out (test) set' for Neur IPS, Twitter, and Wikipedia datasets. While a test set is clearly defined, there is no explicit mention of a separate 'validation' set or how hyperparameter tuning was performed using such a split. |
| Hardware Specification | No | The paper states, 'Hardware specifications are given in the supplementary material.' However, these details are not provided within the main text of the paper. |
| Software Dependencies | No | The paper mentions using 'Adam, (Kingma & Ba, 2015)' as an optimizer and that Sparse LDA is 'implemented in the MALLET framework'. However, it does not provide specific version numbers for these or any other software components. |
| Experiment Setup | Yes | Input: B: Batch size Input: T: Number of topics... We use Adam, (Kingma & Ba, 2015), in the experiments... The synthetic documents were generated using the LDA model: ... symmetric Dirichlet with the standard concentration parameter α = 1/T, and 30 tokens were sampled... Sparse LDA was run with 4 threads... We used M = 1000 as the dimension of the random projection... The Sparse LDA algorithm was run in two modes: With the true hyperparameters, α = 1/T, corresponding to the true α of the corpus, and with topic sparsity parameter β = 1/N, a standard setting... also evaluated Sparse LDA with a modified hyperparameter α = 10/T, and same β... All algorithms were run 5 times, until convergence... All models were run to convergence. |