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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Minimax Classification with 0-1 Loss and Performance Guarantees
Authors: Santiago Mazuelas, Andrea Zanoni, Aritz Pérez
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also present MRCs finite-sample generalization bounds in terms of training size and smallest minimax risk, and show their competitive classification performance w.r.t. state-of-the-art techniques using benchmark datasets. |
| Researcher Affiliation | Academia | Santiago Mazuelas BCAM-Basque Center for Applied Mathematics and IKERBASQUE-Basque Foundation for Science Bilbao, Spain EMAIL Andrea Zanoni École Polytechnique Fédérale de Lausanne Lausanne, Switzerland EMAIL Aritz Pérez BCAM-Basque Center for Applied Mathematics Bilbao, Spain EMAIL |
| Pseudocode | Yes | Algorithm 1 Pseudocode for MRC learning |
| Open Source Code | Yes | Python code with the proposed MRC is provided in https://github.com/Machine Learning BCAM/Minimax-risk-classifiers-Neur IPS-2020 with the settings used in these experimental results. |
| Open Datasets | Yes | In this section we show numerical results for MRCs using 8 UCI datasets for multi-class classification. [...] In the first set of experimental results, we use Adult and Magic data sets from the UCI repository. [...] In the second set of experimental results, we use 6 data sets from the UCI repository (first column of Table 1). |
| Dataset Splits | Yes | The errors and standard deviations in Table 1 have been estimated using paired and stratified 10-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or specific computer specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions 'CVX package' but does not specify its version number. It also mentions 'scikit-learn package' without a version, and mentions 'publicly available code' for AMC and MEM implementations without detailing their specific software dependencies and versions. |
| Experiment Setup | Yes | We obtain up to k = 200/|Y| thresholds using one-dimensional decision trees (decision stumps) so that the feature mapping has up to m = 200 + |Y| components, and we solve the optimization problems at learning with the constraints corresponding to the r = n matrices Φi = Φxi, i = 1, 2, . . ., n, obtained from the n training instances. For all datasets, interval estimates for feature mapping expectations were obtained using (2) with λ(i) = 0.25 for i = 1, 2, . . . , m. |