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..
Inference in Graphical Models via Semidefinite Programming Hierarchies
Authors: Murat A. Erdogdu, Yash Deshpande, Andrea Montanari
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we present numerical experiments with PSOS by solving problems of size up to 10, 000 within several minutes. |
| Researcher Affiliation | Collaboration | Murat A. Erdogdu Microsoft Research EMAIL Yash Deshpande MIT and Microsoft Research EMAIL Andrea Montanari Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1: Partial-SOS, Algorithm 2: CLAP: Confidence Lift And Project |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the public availability of the source code for the described methodology. |
| Open Datasets | No | The paper uses generated data for image denoising (100x100 binary images with Bernoulli or blockwise noise) and Ising spin glasses (two-dimensional grids with i.i.d. parameters), but does not provide specific access information (link, DOI, formal citation) to a publicly available dataset for reproduction. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | In applying Algorithm 1, we add diagonals to the grid (see right plot in Figure 1) in order to satisfy the condition (3.1) with corresponding weight e_d. The model parameter 0 is chosen in each case as to approximately optimize the performances under BP denoising. We use an inertia of 0.5 to help convergence [YFW05], and threshold the marginals at 0.5. Similar to BP-SP, we use an inertia of 0.5. We use plaquettes in the grid (contiguous groups of four vertices) as the largest regions, and apply message passing with inertia 0.1 [WF01]. We find that there is little or no improvement beyond r = 10 (cf. Figure 2). |