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..
beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data
Authors: Valentina Zantedeschi, Rémi Emonet, Marc Sebban
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | finally, we report experiments in semi-supervised learning and learning with label noise, conducted on classical datasets from the UCI repository [15], in order to compare our algorithm with the state of the art approaches. |
| Researcher Affiliation | Academia | Univ Lyon, UJM-Saint-Etienne, CNRS, Institut d Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023, SAINT-ETIENNE, France |
| Pseudocode | No | The paper describes the iterative algorithm in Section 3 in paragraph text, but it does not present it as a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper provides a personal website link (http://vzantedeschi.com/) which does not directly lead to the source code for the methodology described. It also states the implementation details: 'The iterative algorithm with β-SVM is implemented in Python using Cvxopt (for optimizing β-SVM ) and Cvxpy 2 with its Ecos solver [9].', but does not explicitly provide access to their own source code. |
| Open Datasets | Yes | conducted on classical datasets from the UCI repository [15] |
| Dataset Splits | Yes | For each proportion of labeled data, we perform a 4-fold cross-validation and we show the average accuracy over 10 iterations. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | Yes | The iterative algorithm with β-SVM is implemented in Python using Cvxopt (for optimizing β-SVM ) and Cvxpy 2 with its Ecos solver [9]. |
| Experiment Setup | Yes | Concerning the hyper-parameters of the different methods, we fix c2 of β-SVM to c1 ml m , c1 of Well SVM to 1 as explained in [14] and all the other hyper-parameters (c1 for β-SVM and c2 for Well SVM) are tuned by cross-validation through grid search. As for the stopping criteria, we fix ϵ of β-SVM to 10 5 + 10 3 h F and ϵ of Well SVM to 10 3 and the maximal number of iterations to 20 for both methods. |