Committee Selection with Intraclass and Interclass Synergies
Authors: Rani Izsak, Nimrod Talmon, Gerhard Woeginger
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We model both intraclass relations and interclass relations by functions, which we refer to as synergy functions, and study the computational complexity of identifying the best committee, taking into account those synergy functions. Our model accommodates both positive and negative relations between alternatives; further, our efficient algorithms can also deal with a rich class of diversity wishes, which we show how to model using synergy functions. We show that, while the corresponding optimization problem is generally intractable (specifically, for general synergy functions the problem is not even fixed-parameter tractable for the number of classes; roughly speaking, this means that even with very few classes no efficient algorithm for selecting an optimal committee exists), there are various cases for which efficient algorithms do exist. Specifically, we describe efficient algorithms that can identify committees satisfying certain diversity wishes, as well as committees that optimize certain non-trivial positive and negative synergies between the alternatives. |
| Researcher Affiliation | Academia | Rani Izsak Weizmann Institute of Science Rehovot, Israel ran.izsak@weizmann.ac.il Nimrod Talmon Weizmann Institute of Science Rehovot, Israel nimrodtalmon77@gmail.com Gerhard J. Woeginger RWTH Aachen Aachen, Germany woeginger@algo.rwth-aachen.de |
| Pseudocode | No | The paper describes algorithms (e.g., dynamic programming in Theorem 2 and Theorem 6) in prose, detailing the steps and logic. However, it does not provide any formally structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and focuses on computational complexity and algorithm design. It does not mention the release of any source code for the methodology described. |
| Open Datasets | No | The paper is purely theoretical and does not involve the use of datasets for training or evaluation. Therefore, there is no mention of publicly available datasets or access information for them. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data. Consequently, there is no mention of training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and computational complexity analysis. It does not describe any experimental execution or specific hardware used. |
| Software Dependencies | No | The paper discusses algorithmic approaches and mathematical formulations, but it does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or solvers) that would be needed to replicate any experimental work. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments. Therefore, there are no details provided regarding experimental setup, hyperparameters, or training configurations. |