Look-Ahead with Mini-Bucket Heuristics for MPE
Authors: Rina Dechter, Kalev Kask, William Lam, Javier Larrosa
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we present the experiments and discussion. and Experimental Evaluation |
| Researcher Affiliation | Academia | Rina Dechter, Kalev Kask, William Lam University of California, Irvine Irvine, California, USA Javier Larrosa UPC Barcelona Tech Barcelona, Spain |
| Pseudocode | Yes | Algorithm 1: Bucket Error Evaluation (BEE) and Algorithm 2: Minimal Pruned Look-Ahead Subtree |
| Open Source Code | No | No explicit statement or link is provided for open-source code related to the paper's methodology. |
| Open Datasets | Yes | Benchmarks. Includes instances from genetic linkage analysis (pedigree, large Fam) and medical diagnosis (promedas) (see Table 2). and Table 2: Benchmark statistics. |
| Dataset Splits | No | The paper uses benchmark instances but does not provide specific train/validation/test dataset splits. |
| Hardware Specification | No | The paper mentions a memory limit of 4Gb for constructing the MBE heuristic, but does not specify any particular hardware components (e.g., GPU, CPU models) used for running experiments. |
| Software Dependencies | No | The paper mentions that 'Current implementations of AOBB (Kask and Dechter 2001; Marinescu and Dechter 2009) are guided by the mini-bucket heuristic' but does not provide specific software dependencies with version numbers for its own experimental setup. |
| Experiment Setup | Yes | We tried 2-3 different i-bounds for each problem instance... Within each i-bound setting, we varied the look-ahead depth from 0 to 6. and We run Algorithm 2 for pre-processing, yielding a minimal pruned look-ahead subtree for each variable, When the BEE computation (and its table) gets too large (e.g. over 106) we sample (e.g. 105 assignments). |