Bounding the Cost of Search-Based Lifted Inference
Authors: David B. Smith, Vibhav G. Gogate
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We ran our Rao-Blackwellised Importance Sampler on three benchmark SRMs and datasets: (1) The friends, smokers and Asthma MLN and dataset described in [19], (2) The web KB MLN for collective classification and (3) The Protein MLN, in which the task is to infer protein interactions from biological data. All models are available from www.alchemy.cs.washington.edu. [...] Figure 2 shows the sample variance of the estimators as a function of time. |
| Researcher Affiliation | Academia | David Smith University of Texas At Dallas 800 W Campbell Rd, Richardson, TX 75080 dbs014200@utdallas.edu Vibhav Gogate University of Texas At Dallas 800 W Campbell Rd, Richardson, TX 75080 vibhav.gogate@utdallas.edu |
| Pseudocode | Yes | Algorithm 1 Function eval Node(And) [...] Algorithm 2 Function eval Node(Or) [...] Algorithm 3 Function count Path Leaves [...] Algorithm 4 Function make Rao Function [...] Algorithm 5 Function eval Rao Function |
| Open Source Code | No | The paper states 'All models are available from www.alchemy.cs.washington.edu', which refers to the models/datasets used, not the source code for the methodology described in the paper. No other explicit statement or link for the authors' source code is provided. |
| Open Datasets | Yes | All models are available from www.alchemy.cs.washington.edu. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'Markov Logic Network (MLN)' and 'Alchemy', but does not provide specific version numbers for these or any other ancillary software dependencies used in the experiments. |
| Experiment Setup | Yes | For each model, we set 10% randomly selected ground atoms as evidence, and designated them to have True value. We then estimated the partition function via our Rao-Blackwellised sampler with complexity bounds t0, 10, 100, 1000u (bound of 0 yields the LIS algorithm). We used the uniform distribution as our proposal. We ran each sampler 50 times and computed the sample variance of the estimates. |