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..
Estimating Latent-Variable Graphical Models using Moments and Likelihoods
Authors: Arun Tejasvi Chaganty, Percy Liang
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 4. Comparison of parameter estimation error (ˆθ − θ 2) versus error in moments (ϵ) for a hidden Markov model with k = 2 hidden and d = 5 observed values. Empirical moments c M123 were generated by adding Gaussian noise, N(0, ϵI), to expected moments M123. Results are averaged over 400 trials. |
| Researcher Affiliation | Academia | Arun Tejasvi Chaganty EMAIL Percy Liang EMAIL Stanford University, Stanford, CA, USA |
| Pseudocode | Yes | Algorithm 1 GETMARGINALS (pseudoinverse) |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the methodology described, nor does it provide any links to a code repository. |
| Open Datasets | No | Empirical moments c M123 were generated by adding Gaussian noise, N(0, ϵI), to expected moments M123. |
| Dataset Splits | No | The paper discusses the comparison of statistical efficiency between estimators using generated empirical moments over 'n data points' (Section 4.3), but it does not specify train/validation/test dataset splits for reproduction. |
| Hardware Specification | No | The paper mentions numerical experiments were performed and results averaged over 400 trials, but it does not provide any specific hardware details such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper refers to optimization procedures like 'EM or LBFGS' but does not specify any software libraries, packages, or their version numbers that were used for implementation or experimentation. |
| Experiment Setup | Yes | Comparison of parameter estimation error (ˆθ − θ 2) versus error in moments (ϵ) for a hidden Markov model with k = 2 hidden and d = 5 observed values. Empirical moments c M123 were generated by adding Gaussian noise, N(0, ϵI), to expected moments M123. |