Credal Marginal MAP
Authors: Radu Marinescu, Debarun Bhattacharjya, Junkyu Lee, Fabio Cozman, Alexander Gray
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An extensive empirical evaluation demonstrates the effectiveness of our new methods on random as well as real-world benchmark problems. We evaluate empirically the new CMMAP inference algorithms on random credal networks with different graph topologies as well as a collection of credal networks derived from real-world applications. |
| Researcher Affiliation | Collaboration | Radu Marinescu IBM Research, Ireland radu.marinescu@ie.ibm.com Debarun Bhattacharjya IBM Research, USA debarunb@us.ibm.com Junkyu Lee IBM Research, USA junkyu.lee@ibm.com Alexander Gray IBM Research, USA alexander.gray@ibm.com Fabio Cozman Universidade de São Paulo, Brazil fgcozman@usp.br |
| Pseudocode | Yes | Algorithm 1 Variable Elimination for Credal Marginal MAP; Algorithm 2 Mini-Buckets for Credal Marginal MAP; Algorithm 3 Local Search for Credal Marginal MAP |
| Open Source Code | Yes | The supplementary material includes additional details, experimental results, code and benchmarks. |
| Open Datasets | Yes | We evaluate the proposed algorithms for CMMAP on random credal networks and credal networks derived from real-world applications. ... credal networks derived from 22 real-world Bayesian networks1 ... 1Available at https://www.bnlearn.com/bnrepository/ |
| Dataset Splits | No | The paper does not provide specific details on training, validation, and test dataset splits (percentages, counts, or specific methods like k-fold cross-validation). |
| Hardware Specification | Yes | All competing algorithms were implemented in C++ and the experiments were run on a 32-core machine with 128GB of RAM running Ubuntu Linux 20.04. |
| Software Dependencies | No | The paper mentions 'C++' and 'Ubuntu Linux 20.04' but does not specify version numbers for any libraries, solvers, or other key software dependencies. |
| Experiment Setup | Yes | The local search algorithms used N = 10 iterations and M = 10, 000 maximum flips per iteration, and they all used the approximate L2U algorithm with 10 iterations [29] to evaluate the MAP assignments during search. Furthermore, for SHC we set the flip probability pflip to 0.2, TS used a taboo list of size 100, while for SA we set the initial temperature and cooling schedule to Tinit = 100 and σ = 0.9, respectively. For CMBE(i) we set the i-bound i to 2 and used the same L2U algorithm to evaluate the solution found. All competing algorithms were allocated a 1 hour time limit and 8GB of memory per problem instance. |