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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
On Constrained Open-World Probabilistic Databases
Authors: Tal Friedman, Guy Van den Broeck
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,603 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,026 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To illustrate the effect this has, consider a schema in which we have 3 relations: Li LA(x) denoting whether one lives in Los Angeles, Li Spr(x) denoting whether one lives in Spring๏ฌeld, and S(x) denoting whether one is a scientist. Using a vocabulary of 500 people where each person is present in at most one relation, Table 1 shows the resulting upper probability bound under different model assumptions, where the constrained open-world restricts at most 50% of mass on Li LA, 5% on S, and 0.5% on Li Spr. |
| Researcher Affiliation | Academia | Tal Friedman and Guy Van den Broeck University of California, Los Angeles EMAIL |
| Pseudocode | Yes | Algorithm 1 Lift R(Q, P), abbreviated by L(Q) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions 'Using a vocabulary of 500 people' for an example, but does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for a publicly available dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |