Pushing Forward Marginal MAP with Best-First Search
Authors: Radu Marinescu, Rina Dechter, Alexander Ihler
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyze the potential relative benefits of several bestfirst search algorithms and demonstrate their effectiveness against recent branch and bound schemes through extensive empirical evaluations. Our results show that best-first search improves significantly over existing depth-first approaches, in many cases by several orders of magnitude, especially when guided by relatively weak heuristics. |
| Researcher Affiliation | Collaboration | Radu Marinescu IBM Research Ireland radu.marinescu@ie.ibm.com Rina Dechter and Alexander Ihler University of California, Irvine Irvine, CA 92697, USA {dechter,ihler}@ics.uci.edu |
| Pseudocode | Yes | Algorithm 1: AOBF-MMAP |
| Open Source Code | No | No statement or link is provided indicating the release of the source code for the methodology. |
| Open Datasets | Yes | We evaluate empirically the proposed best-first search algorithms on problem instances derived from benchmarks used in the PASCAL2 Inference Challenge [Elidan et al., 2012]. |
| Dataset Splits | No | The paper describes generating 'easy' and 'hard' problem instances for evaluation, but does not specify traditional training, validation, or test splits of a dataset as would be typical for machine learning model training. |
| Hardware Specification | Yes | All algorithms were implemented in C++ (64-bit) and the experiments were run on a 2.6GHz 8-core processor with 80 GB of RAM. |
| Software Dependencies | No | All algorithms were implemented in C++ (64-bit) |
| Experiment Setup | Yes | The weighted mini-bucket heuristics were generated in a preprocessing phase, prior to search, using uniform weights. |