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..
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning
Authors: Francesco Orabona
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Optimal rates of convergence are proved under standard smoothness assumptions on the target function as well as preliminary empirical results. and Even if this is mainly a theoretical work, we believe that it might also have a big potential in the applied world. Hence, as a proof of concept on the potentiality of this method we have also run a few preliminary experiments, to compare the performance of Pi STOL to an SVM using 5-folds cross-validation to select the regularization weight parameter. |
| Researcher Affiliation | Collaboration | Francesco Orabona Yahoo! Labs New York, USA EMAIL Work done mainly while at Toyota Technological Institute at Chicago. |
| Pseudocode | Yes | Algorithm 3 Pi STOL: Parameter-free STOchastic Learning. |
| Open Source Code | No | No explicit statement or link to the open-source code for the methodology described in this paper was found. The paper mentions using 'LIBSVM' which is a third-party tool. |
| Open Datasets | Yes | Datasets available at http://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/. |
| Dataset Splits | Yes | to compare the performance of Pi STOL to an SVM using 5-folds cross-validation to select the regularization weight parameter. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instance types) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The latest version of LIBSVM was used to train the SVM [10]. |
| Experiment Setup | Yes | to compare the performance of Pi STOL to an SVM using 5-folds cross-validation to select the regularization weight parameter. The experiments were repeated with 5 random shuf๏ฌes, showing the average and standard deviations over three datasets. ... Pi STOL closely tracks the performance of the tuned SVM when a Gaussian kernel is used. ... Note that in the case of News20, a linear kernel is used over the vectors of size 1355192. ... our unoptimized Matlab implementation of Pi STOL |