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..
On Learning Ising Models under Huber's Contamination Model
Authors: Adarsh Prasad, Vishwak Srinivasan, Sivaraman Balakrishnan, Pradeep Ravikumar
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate our theoretical results by simulations. |
| Researcher Affiliation | Academia | Machine Learning Department Department of Statistics and Data Science Carnegie Mellon University Pittsburgh, PA 15213 |
| Pseudocode | Yes | Algorithm 1 Robust1DMean Robust univariate mean estimator |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper describes synthetic experiments where graphs are constructed with varying parameters, but it does not use or provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes synthetic experiments and simulations, but it does not specify train, validation, and test dataset splits as typically done for machine learning experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library names, solvers, frameworks). |
| Experiment Setup | Yes | We generate our plots in the following manner: first we construct two graphs with the same structure either from Gclique p,d of Gstar p,d . We instantiate parameters for the first graph with θ(1) with model width ω and then vary the parameters for the second graph as θ(2) = θ(1) i 25 for i ranging from 1 to 50. We vary p {12, 15}, d {3 : 8 : 1} and ω {0.2 : 1.0 : 0.2} {1.5 : 10 : 0.5} where {a : b : c} denotes values between a and b (both inclusive) with consecutive values differing by c. |