Generalization Properties and Implicit Regularization for Multiple Passes SGM
Authors: Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We carry out some numerical simulations to illustrate our results. The experiments are executed 10 times each, on the benchmark datasets reported in Table 1 |
| Researcher Affiliation | Academia | Junhong Lin JHLIN5@HOTMAIL.COM Raffaello Camoriano , , RAFFAELLO.CAMORIANO@IIT.IT Lorenzo Rosasco , LROSASCO@MIT.EDU LCSL, Massachusetts Institute of Technology and Istituto Italiano di Tecnologia, Cambridge, MA 02139, USA DIBRIS, Universit a degli Studi di Genova, Via Dodecaneso 35, Genova, Italy i Cub Facility, Istituto Italiano di Tecnologia, Via Morego 30, Genova, Italy |
| Pseudocode | Yes | Algorithm 1. Given a sample z, the stochastic gradient method (SGM) is defined by w1 = 0 and wt+1 = wt ηt V (yjt, wt, Φ(xjt) )Φ(xjt), t = 1, . . . , T, for a non-increasing sequence of step-sizes {ηt > 0}t N and a stopping rule T N. |
| Open Source Code | Yes | Code: lcsl.github.io/Multiple Passes SGM (Footnote 4) |
| Open Datasets | Yes | Datasets: archive.ics.uci.edu/ml and www.csie.ntu.edu.tw/~cjlin/libsvmtools/ datasets/ (Footnote 5) |
| Dataset Splits | Yes | In order to apply hold-out cross-validation, the training set is split in two parts: one for empirical risk minimization and the other for validation error computation (80% 20%, respectively). |
| Hardware Specification | Yes | The experimental platform is a server with 12 Intel Xeon E5-2620 v2 (2.10GHz) CPUs and 132 GB of RAM. |
| Software Dependencies | No | The paper mentions using LIBSVM and implementing in Python (via a code link), but it does not specify version numbers for any software libraries, frameworks, or tools used in the experiments. |
| Experiment Setup | Yes | In the first experiment, the SIGM step-size is fixed as η = 1/ n. In the second experiment, we consider SIGM with decaying stepsize (η = 1/4 and θ = 1/2). |