Approximate Inference via Weighted Rademacher Complexity
Authors: Jonathan Kuck, Ashish Sabharwal, Stefano Ermon
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 jkuck@cs.stanford.edu Ashish Sabharwal Allen Institute for Artiļ¬cial Intelligence Seattle, WA ashishs@allenai.org Stefano Ermon Computer Science Department Stanford University ermon@cs.stanford.edu |
| 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. |