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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Pushing Forward Marginal MAP with Best-First Search
Authors: Radu Marinescu, Rina Dechter, Alexander Ihler
IJCAI 2015 | Venue PDF | 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 EMAIL Rina Dechter and Alexander Ihler University of California, Irvine Irvine, CA 92697, USA EMAIL |
| 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. |