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..
Implicit Regularization of Accelerated Methods in Hilbert Spaces
Authors: Nicolò Pagliana, Lorenzo Rosasco
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results are validated by numerical simulations. and In this section we show some numerical simulations to validate our results. |
| Researcher Affiliation | Academia | Nicolò Pagliana University of Genoa DIMA & Ma LGa EMAIL Lorenzo Rosasco University of Genoa DIBRIS, Ma LGa, IIT & MIT EMAIL |
| Pseudocode | No | The paper describes algorithms using mathematical equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making its source code available or provide any links to a code repository. |
| Open Datasets | Yes | In Figure 2 we show the test error related to the real dataset pumadyn8nh (available at https://www.dcc.fc.up.pt/ ltorgo/Regression/puma.html). |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit references to predefined splits). |
| Hardware Specification | Yes | We gratefully acknowledge the support of NVIDIA Corporation for the donation of the Titan Xp GPUs and the Tesla k40 GPU used for this research. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, or specific solver versions). |
| Experiment Setup | Yes | The parameters are chosen N = 10^4, n = 10^2, γ = 1, σ = 0.5. |