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..
Approximate Inference via Weighted Rademacher Complexity
Authors: Jonathan Kuck, Ashish Sabharwal, Stefano Ermon
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that we can produce tighter bounds than competing methods in both the weighted and unweighted settings. |
| Researcher Affiliation | Collaboration | Jonathan Kuck Computer Science Department Stanford University EMAIL Ashish Sabharwal Allen Institute for Arti๏ฌcial Intelligence Seattle, WA EMAIL Stefano Ermon Computer Science Department Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 Rademacher Estimate of log Z(w) |
| Open Source Code | No | The paper does not provide explicit links or statements regarding the open-source code for the methodology it describes. |
| Open Datasets | Yes | The models used in our experiments can be downloaded from http://reasoning.cs.ucla.edu/c2d/results.html |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits, percentages, or sample counts, nor does it refer to predefined splits with citations for reproducibility. |
| Hardware Specification | No | The paper mentions using specific software implementations (python maxflow module, Max HS solver) but does not provide any specific hardware details such as GPU/CPU models or memory used for the experiments. |
| Software Dependencies | No | The paper mentions software like 'python maxflow module' and 'Max HS' but does not specify their version numbers or other software dependencies with versioning for reproducibility. |
| Experiment Setup | Yes | Figure 2: Bounds for a 7x7 spin glass model with k = 5 (for both methods), that hold with probability .95. |