Pareto Optimal Allocation under Uncertain Preferences
Authors: Haris Aziz, Ronald de Haan, Baharak Rastegari
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that for both the lottery model and the joint probability model, Exists Certainly PO-Assignment is NP-complete. We also prove that Assignment With Highest POProbability is NP-hard for both models. On the other hand, we show that for a general class of independent uncertainty models, both problems Is PO-Probability Non Zero and Is PO-Probability One can be solved in linear time. Whereas PO-Probability is polynomial-time solvable for the joint probability model, we prove that the problem is #Pcomplete for the lottery model. |
| Researcher Affiliation | Academia | Haris Aziz Data61, CSIRO and UNSW Sydney, Australia haris.aziz@data61.csiro.au Ronald de Haan University of Amsterdam Amsterdam, the Netherlands R.de Haan@uva.nl Baharak Rastegari University of Bristol Bristol, UK baharak.rastegari@bristol.ac.uk |
| Pseudocode | No | The paper describes algorithms and proofs in prose but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve experimental evaluation with datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |