PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences
Authors: Róbert Busa-Fekete, Balázs Szörényi, Eyke Hüllermeier
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also present first experiments to illustrate the practical performance of our methods. |
| Researcher Affiliation | Collaboration | R obert Busa-Fekete MTA-SZTE Research Group on Artificial Intelligence, Hungary busarobi@inf.u-szeged.hu Bal azs Sz or enyi INRIA Lille, Seque L project, France szorenyi@inf.u-szeged.hu Eyke H ullermeier University of Paderborn, Germany eyke@upb.de |
| Pseudocode | Yes | Algorithm 1 RANKEL (Y1,1, . . . , YK,K, ρ, δ, ϵ) 1: for i, j = 1 K do Initialization 2: yi,j = 0, ni,j = 0 3: A = {(i, j)|i = j, 1 i, j K} 4: t = 0 5: repeat 6: for (i, j) A do 7: y Yi,j Draw a random sample 8: ni,j = ni,j + 1 9: Keep track the number of samples drawn for each Yi,j 10: Update yi,j with y 11: Y = [ yi,j]K K Y = [yi,j]K K 12: t = t + 1 13: A = SAMPLINGSTRATEGY( Y, N, δ, ϵ, t, ρ) 14: until 0 < |A| 15: τ = GETESTIMATEDRANKING( Y, N, δ, ϵ, t) Calculate a ranking based on Y by using R 16: return τ |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code or provide a link to a code repository for the methodology described. |
| Open Datasets | No | The paper states, 'To illustrate our PAC rank elicitation method, we applied it to sports data, namely the soccer matches of the last ten seasons of the German Bundesliga.' However, it does not provide a specific link, DOI, repository name, or formal citation for this dataset or the processed version they used, nor does it explicitly state it is publicly available in a manner that allows access. |
| Dataset Splits | No | The paper describes applying its method to soccer match data but does not specify any training, validation, or testing splits or percentages for the dataset used in its experiments. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies or version numbers (e.g., programming language versions, library versions, or specific solver versions) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, model initialization, or specific training configurations for its experiments. |