Learning with Incremental Iterative Regularization
Authors: Lorenzo Rosasco, Silvia Villa
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments |
| Researcher Affiliation | Academia | Lorenzo Rosasco DIBRIS, Univ. Genova, ITALY; LCSL, IIT & MIT, USA; Silvia Villa LCSL, IIT & MIT, USA |
| Pseudocode | No | The paper describes the algorithm using mathematical equations (7) and (8) but does not present it as a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide links to a code repository. |
| Open Datasets | Yes | cpu Small3, Adult and Breast Cancer Wisconsin (Diagnostic)4 real-world datasets. 3Available at http://www.cs.toronto.edu/ delve/data/comp-activ/desc.html 4Adult and Breast Cancer Wisconsin (Diagnostic), UCI repository, 2013. |
| Dataset Splits | No | The paper mentions 'Validation Error' and 'Training Error' in Figure 2, but does not provide specific details on how the dataset was split into training, validation, and test sets (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | Let ˆw0 2 H and γ 2 R++. Consider the sequence ( ˆwt)t2N generated through the following procedure:... and The input points (xi)1 i n are uniformly distributed in [0, 1] and the output points are obtained as yi = hw , Φ(xi)i + Ni, where Ni is a Gaussian noise with zero mean and standard deviation 1 and Φ = ('k)1 k d is a dictionary of functions whose k-th element is 'k(x) = cos((k 1)x)+sin((k 1)x). In Figure 1, we plot the test error for d = 5 (with n = 80 in (a) and 800 in (b)). |