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..
A Novel Approach for Constrained Optimization in Graphical Models
Authors: Sara Rouhani, Tahrima Rahman, Vibhav Gogate
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that our algorithms are superior to the following approach: encode the problem as a mixed integer linear program (MILP) and solve the latter using a state-of-the-art MILP solver such as SCIP. |
| Researcher Affiliation | Academia | Sara Rouhani, Tahrima Rahman and Vibhav Gogate The University of Texas at Dallas EMAIL |
| Pseudocode | Yes | Algorithm 1 ANYTIME-CMPE (M1, M2, q, k) |
| Open Source Code | No | The paper mentions using SCIP [13], a state-of-the-art open source MILP solver, but does not provide specific access to the authors' own implementation code for the methodology described. |
| Open Datasets | Yes | We experimented with the following benchmark graphical models, available from the UAI 2010 and 2014 competitions [11, 16] |
| Dataset Splits | No | The paper mentions using benchmark graphical models but does not provide specific information about training, validation, or test dataset splits. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models or memory specifications) used for running the experiments are mentioned in the paper. |
| Software Dependencies | Yes | We compared the performance of Algorithm ANYTIME-CMPE with SCIP [13], a state-of-the-art open source mixed integer linear programming (MILP) solver. ... The SCIP Optimization Suite 7.0. |
| Experiment Setup | Yes | We experimented with the following five values of k: {1, 3, 5, 7, 9}. For each k, we ran our algorithm on each probabilistic network for 1200 seconds. SCIP was also run for 1200 seconds on each network. ... We used restart-based local search to perform search over the value assignments to S. ... We implemented the greedy MCKP algorithm of [14] to solve Ps. ... We used the max-degree heuristic outlined in section 4.2 to select a minimal k-separator. |