Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
On the Consistency of Ordinal Regression Methods
Authors: Fabian Pedregosa, Francis Bach, Alexandre Gramfort
JMLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare this novel surrogate with competing approaches on 9 different datasets. Our method shows to be highly competitive in practice, outperforming the least squares loss on 7 out of 9 datasets. |
| Researcher Affiliation | Academia | Fabian Pedregosa EMAIL INRIA D epartement d informatique de l ENS, Ecole normale sup erieure, CNRS, PSL Research University Paris, France Francis Bach EMAIL INRIA D epartement d informatique de l ENS, Ecole normale sup erieure, CNRS, PSL Research University Paris, France Alexandre Gramfort EMAIL LTCI, T el ecom Paris Tech, Universit e Paris-Saclay INRIA, Universit e Paris-Saclay Saclay, France |
| Pseudocode | No | The paper focuses on theoretical analysis, proofs, and mathematical formulations. It describes algorithms conceptually but does not present any structured pseudocode blocks or clearly labeled algorithm sections. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing the source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | The different datasets that we will consider are described in (Chu and Keerthi, 2005) and can be download from the authors website4. http://www.gatsby.ucl.ac.uk/~chuwei/ordinalregression.html. |
| Dataset Splits | Yes | Performance is computed as the squared error on left out data, averaged over 20 folds. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers (e.g., programming languages, libraries, frameworks) used for the experiments. |
| Experiment Setup | No | The paper states that "the optimal values of w, θ were found by minimizing the empirical surrogate risk" and that "we did not consider the use of regularization." However, it does not provide specific hyperparameters like learning rate, batch size, or optimizer settings needed to reproduce the experiment. |