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 [1].
Anytime Anyspace AND/OR Best-First Search for Bounding Marginal MAP
Authors: Qi Lou, Rina Dechter, Alexander Ihler
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation on three challenging benchmarks demonstrates that our unified best-first search algorithm using pre-compiled variational heuristics often provides tighter anytime upper bounds compared to those state-of-the-art baselines. |
| Researcher Affiliation | Academia | Qi Lou University of California, Irvine Irvine, CA 92697, USA EMAIL Rina Dechter University of California, Irvine Irvine, CA 92697, USA EMAIL Alexander Ihler University of California, Irvine Irvine, CA 92697, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Anytime UBFS for MMAP |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | The benchmark set includes three problem domains: grid networks (grid), medical diagnosis expert systems (promedas), and protein, made from the small protein side-chains of Yanover and Weiss (2002). grid is a subset of the grid dataset used in Marinescu et al. (2017)... promedas is the same dataset as that used in Marinescu et al. (2017). |
| Dataset Splits | No | The paper describes experimental settings like using 50% or 10% of variables as MAX variables but does not specify a training, validation, and test split for the datasets themselves. |
| Hardware Specification | No | The paper specifies memory allocation ("4GB memory") but does not mention specific hardware components such as CPU or GPU models. |
| Software Dependencies | No | The paper mentions that implementations are "in C/C++" but does not specify any software versions for compilers, libraries, or other dependencies. |
| Experiment Setup | Yes | The time budget is set to 1 hour for all experiments. We allot 4GB memory to all algorithms, with 1GB extra memory to AAOBF for caching. |