Sum-Product-Max Networks for Tractable Decision Making
Authors: Mazen Melibari, Pascal Poupart, Prashant Doshi
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a new representation called sum-product-max network (SPMN) that generalizes a sum-product network (SPN) to the class of decision-making problems and whose solution, analogous to DCs, scales linearly in the size of the network. We show that SPMNs may be reduced to DCs linearly and present a first method for learning SPMNs from data. This approach is significant because it facilitates a novel paradigm of tractable decision making driven by data. To evaluate new methods for learning SPMNs in this paper and in the future, we establish an initial testbed of datasets each reflecting a realistic non-sequential decisionmaking problem. We evaluate the Learn SPMN algorithm by applying it to a testbed of 10 data sets whose attributes consist of state and decision variables and corresponding utility values. |
| Researcher Affiliation | Academia | David R. Cheriton School of Computer Science, University of Waterloo, Canada Dept. of Computer Science, University of Georgia, Athens, GA 30602, USA |
| Pseudocode | Yes | Algorithm 1: Learn SPMN; Algorithm 2: SPMN Parameter Learning; Algorithm 3: SPMN EM Up; Algorithm 4: SPMN EM Down; Algorithm 5: SPMN-EM |
| Open Source Code | Yes | [tes, 2016] Evaluation testbed and supplementary file. https://github.com/decisionSPMN, 2016. Accessed: April 20, 2016. |
| Open Datasets | Yes | We evaluate the Learn SPMN algorithm by applying it to a testbed of 10 data sets whose attributes consist of state and decision variables and corresponding utility values. The real-world datasets and associated metadata are available for download [tes, 2016]. |
| Dataset Splits | Yes | MEU for SPMN is the mean of 10-fold cross-validation. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned. The paper discusses using methods like EM and K-means but does not specify software versions. |
| Experiment Setup | No | No specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) are provided. Algorithm 5 mentions 'random Initilization(S)' and a 'repeat...until convergence' loop but no concrete values. |