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..
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
Authors: Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments demonstrate the effectiveness of our approach in practice. |
| Researcher Affiliation | Academia | 1Istituto Italiano di Tecnologia, Genoa, Italy 2University of Genoa, Genoa, Italy 3Imperial College of London, London, United Kingdom 4University College London, London, United Kingdom. |
| Pseudocode | Yes | Algorithm 1 Within-Task Algorithm: SGD on the Biased Regularized True Risk and Algorithm 2 Meta-Algorithm, SGD on ˆE with Subgradients. |
| Open Source Code | Yes | Code available at https://github.com/prolearner/online_LTL |
| Open Datasets | Yes | We run experiments on the computer survey data from (Lenk et al., 1996) |
| Dataset Splits | No | The paper mentions that parameters were "tuned by validation" and describes the generation of synthetic data (e.g., "inputs were uniformly sampled on the unit sphere", "labels were generated as y = hx, wµi + "), but it does not specify explicit train/validation/test split percentages or sample counts for either the synthetic or real datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not specify the version numbers for any software dependencies or libraries used in the implementation of the experiments (e.g., Python version, specific machine learning framework versions). |
| Experiment Setup | Yes | In the regression case, the inputs were uniformly sampled on the unit sphere and the labels were generated as y = hx, wµi + , with sampled from a zero-mean Gaussian distribution, with standard deviation chosen to have signal-to-noise ratio equal to 10 for each task. In all experiments, the regularization parameter λ and the stepsize γ were tuned by validation. |