Algorithms for the Nearest Assignment Problem
Authors: Sara Rouhani, Tahrima Rahman, Vibhav Gogate
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we describe results of our extensive empirical investigation on a large number of benchmark problems from the UAI 2014 probabilistic inference competition. |
| Researcher Affiliation | Academia | Sara Rouhani, Tahrima Rahman, Vibhav Gogate The University of Texas at Dallas {sara.rouhani, tahrima.rahman, vibhav.gogate}@utdallas.edu |
| Pseudocode | Yes | Algorithm 1 INAP: Greedy Algorithm for NAP over IBNs and Algorithm 2 0CNAP 0-cutset based algorithm for solving NAP on Arbitrary Bayesian networks |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We compared our algorithms on the following four types of benchmark Bayesian and Markov networks: (1) Noisy OR Bayesian networks (BN20), (2) Ising Models, (3) Relational Markov Networks, and (4) Image Segmentation. These networks were used in the UAI 2014 and 2016 inference competitions (see [Gogate, 2014]). |
| Dataset Splits | No | The paper mentions using benchmark networks from the UAI 2014 and 2016 inference competitions but does not specify any training, validation, or test dataset splits. It describes generating 'q values' for evaluation, but this is distinct from standard dataset splitting for model training and validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | We ran each algorithm for 20 minutes. [...] We randomly restarted the search if the local minima is reached or 10000 moves have been made, which ever is earlier. [...] We constructed the 0-cutset as follows. We recursively delete a variable having the highest degree until the graph is empty, breaking ties using average log odds (see Theorem 3). |