Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning
Authors: Francesco Orabona
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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 francesco@orabona.com 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 shuffles, 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 |