Pairwise Choice Markov Chains
Authors: Stephen Ragain, Johan Ugander
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that the PCMC model significantly outperforms both the Multinomial Logit (MNL) model and a mixed MNL (MMNL) model in prediction tasks on both synthetic and empirical datasets known to exhibit violations of Luce s axiom. |
| Researcher Affiliation | Academia | Stephen Ragain Management Science & Engineering Stanford University Stanford, CA 94305 sragain@stanford.edu Johan Ugander Management Science & Engineering Stanford University Stanford, CA 94305 jugander@stanford.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Data and code available here: https://github.com/sragain/pcmc-nips |
| Open Datasets | Yes | We evaluate our inference procedure on two empirical choice datasets, SFwork and SFshop, collected from a survey of transportation preferences around the San Francisco Bay Area [16]. |
| Dataset Splits | Yes | The results are averaged over 1,000 different permutations of the data with a 75/25 train/test split employed for each permutation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions using 'SLSQP [25]' for optimization, but it does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | We applied small amounts of additive smoothing to each dataset. We used a discrete mixture of k MNL models (with O(kn) parameters), choosing k so that the MMNL model had strictly more parameters than the PCMC model on each data set. |