Probabilistic Matrix Inspection and Group Scheduling
Authors: Hooyeon Lee, Ashish Goel
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We first show that an intuitive greedy algorithm exists for 1-row and 1-column matrices, and we generalize this to design an algorithm that finds an optimal policy in polynomial time for the general case. ... By abstracting away the details of group scheduling, we are left with a mathematical model that represents the scheduling process, and in this work we study the properties of an optimal inspection policy and design an algorithm that finds it in polynomial time. |
| Researcher Affiliation | Academia | Hooyeon Lee, Ashish Goel, Stanford University |
| Pseudocode | No | The paper describes algorithms in textual form (e.g., 'We inspect the columns in increasing order of µc(1 sc)/sc, and in each column, we inspect the entries of it in increasing order of probabilities.') but does not include structured pseudocode blocks or algorithm figures. |
| Open Source Code | No | The paper does not provide any statement about making its source code available or include links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not utilize datasets for empirical evaluation. The numerical example in Section 4.3 is a constructed illustration, not a dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for computational work. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies or their version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |