Learning Mixed Multinomial Logit Model from Ordinal Data
Authors: Sewoong Oh, Devavrat Shah
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a sufficient condition as well as an efficient algorithm for learning mixed MNL models from partial preferences/comparisons data. The algorithms proposed are iterative, and primarily based on spectral properties of underlying tensors/matrices with provable, fast convergence guarantees. That is, algorithms are not only polynomial time, they are practical enough to be scalable for high dimensional data sets. |
| Researcher Affiliation | Academia | Sewoong Oh Dept. of Industrial and Enterprise Systems Engr. University of Illinois at Urbana-Champaign Urbana, IL 61801 swoh@illinois.edu Devavrat Shah Department of Electrical Engineering Massachussetts Institute of Technology Cambridge, MA 02139 devavrat@mit.edu |
| Pseudocode | Yes | Algorithm 1, Algorithm 2 SPECTRALDIST: Moment method for Mixture of Discrete Distribution [12], Algorithm 3 MATRIXALTMIN: Alternating Minimization for Matrix Completion [12], Algorithm 4 TENSORLS: Least Squares method for Tensor Estimation [12] |
| Open Source Code | No | The paper does not contain any statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper discusses 'samples' and 'observations' in a theoretical context but does not refer to any specific publicly available or open datasets used for empirical evaluation, nor does it provide links or citations to such data. |
| Dataset Splits | No | The paper focuses on theoretical conditions and algorithms, and therefore does not provide specific details on dataset splits (training, validation, or test) as it does not conduct empirical experiments with data. |
| Hardware Specification | No | The paper describes a theoretical framework and algorithms without conducting empirical experiments, thus it does not specify any hardware used for computations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers required to replicate the work. |
| Experiment Setup | No | The paper presents theoretical algorithms and their analysis, and as such, it does not include details on an experimental setup, hyperparameters, or training configurations. |