On Constrained Open-World Probabilistic Databases
Authors: Tal Friedman, Guy Van den Broeck
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 Springļ¬eld, 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 {tal, guyvdb}@cs.ucla.edu |
| 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. |