Implicit Regularization of Accelerated Methods in Hilbert Spaces
Authors: Nicolò Pagliana, Lorenzo Rosasco
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 pagliana@dima.unige.it Lorenzo Rosasco University of Genoa DIBRIS, Ma LGa, IIT & MIT lrosasco@mit.edu |
| 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. |