Minimax Classification with 0-1 Loss and Performance Guarantees
Authors: Santiago Mazuelas, Andrea Zanoni, Aritz Pérez
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 smazuelas@bcamath.org Andrea Zanoni École Polytechnique Fédérale de Lausanne Lausanne, Switzerland andrea.zanoni@epfl.ch Aritz Pérez BCAM-Basque Center for Applied Mathematics Bilbao, Spain aperez@bcamath.org |
| 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. |