Parameter Estimation for Generalized Thurstone Choice Models
Authors: Milan Vojnovic, Seyoung Yun
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1. Mean square error for two different generalized Thurstone choice models TF : (left) F is a double-exponential distribution, and (right) F is a uniform distribution. The vertical bars denote 95% confidence intervals. The results confirm two qualitatively different relations with the cardinality of comparison sets as suggested by the theory. |
| Researcher Affiliation | Industry | Milan Vojnovic MILANV@MICROSOFT.COM Microsoft Research, Cambridge, UK |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes generating synthetic comparison sets by 'independent uniform random samples from the set of all items' for its numerical examples, but it does not use or provide concrete access information for a publicly available dataset. |
| Dataset Splits | No | The paper describes a simulation experiment but does not provide specific details on training, validation, or test dataset splits. It describes generation of synthetic data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We fix the values of the number of items n and the number of comparisons m, and consider a choice of a generalized Thurstone model TF for the value of parameter = 0. We consider comparison sets of the same cardinality of value k that are independent uniform random samples from the set of all items. For every fixed value of k, we run 100 repetitions to estimate the mean square error. We do this for the distribution of noise according to a double-exponential distribution (Bradley-Terry model) and according to a uniform distribution, both with unit variance. |