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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Generalization Properties and Implicit Regularization for Multiple Passes SGM
Authors: Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco
ICML 2016 | Venue PDF | LLM Run Details | Input Tokens: 16,872 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,061 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Raffaello Camoriano , , EMAIL Lorenzo Rosasco , EMAIL 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). |