Efficient and Accurate Learning of Mixtures of Plackett-Luce Models
Authors: Duc Nguyen, Anderson Y. Zhang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real datasets show that our algorithm is competitive in terms of accuracy and speed to baseline algorithms, especially on datasets with a large number of items. |
| Researcher Affiliation | Academia | Duc Nguyen1, Anderson Y. Zhang2 1 Depart of Computer and Information Science, University of Pennsylvania 2 Department of Statistics and Data Science, University of Pennsylvania |
| Pseudocode | Yes | Algorithm 1 Spectral Clustering with Adaptive Dimension Reduction, Algorithm 2 Least Squares Parameter Estimation, Algorithm 3 Spectral Initialization, Algorithm 4 Weighted Luce Spectral Ranking, Algorithm 5 Spectral EM (EM-LSR). |
| Open Source Code | No | The paper does not explicitly state that the source code for the described methodology is released or provide a direct link to a code repository. |
| Open Datasets | Yes | We include commonly used datasets in previous works such as APA, Irish Elections (West, North, Meath) and SUSHI all with n < 15. We perform additional experiments on the ML-10M movie ratings datasets (Harper and Konstan 2015). |
| Dataset Splits | Yes | We partition all the rankings with a 80-20 training-testing split; and the train rankings into 80% for inference and 20% for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | Yes | efficiently done using off-the-shelf solvers (Virtanen et al. 2020). Virtanen, P.; Gommers, R.; Oliphant, T. E.; Haberland, M.; Reddy, T.; Cournapeau, D.; Burovski, E.; Peterson, P.; Weckesser, W.; Bright, J.; van der Walt, S. J.; Brett, M.; Wilson, J.; Millman, K. J.; Mayorov, N.; Nelson, A. R. J.; Jones, E.; Kern, R.; Larson, E.; Carey, C. J.; Polat, I.; Feng, Y.; Moore, E. W.; Vander Plas, J.; Laxalde, D.; Perktold, J.; Cimrman, R.; Henriksen, I.; Quintero, E. A.; Harris, C. R.; Archibald, A. M.; Ribeiro, A. H.; Pedregosa, F.; van Mulbregt, P.; and Sci Py 1.0 Contributors. 2020. Sci Py 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17: 261 272. |
| Experiment Setup | Yes | We set n = 100 and L = 5 while varying the number of mixture components K for different experiments. We partition all the rankings with a 80-20 training-testing split; and the train rankings into 80% for inference and 20% for validation. K is chosen using Bayesian Information Criterion (Gelman, Hwang, and Vehtari 2014) on the validation set and the log-likelihood of the final model is evaluated using the test set. To keep a fair comparison, we use spectral initialization for all algorithms. |