Anytime Anyspace AND/OR Best-First Search for Bounding Marginal MAP
Authors: Qi Lou, Rina Dechter, Alexander Ihler
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 qlou@ics.uci.edu Rina Dechter University of California, Irvine Irvine, CA 92697, USA dechter@ics.uci.edu Alexander Ihler University of California, Irvine Irvine, CA 92697, USA ihler@ics.uci.edu |
| 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. |