Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning
Authors: François Denis, Mattias Gybels, Amaury Habrard
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that these bounds are tight and that they significantly improve existing bounds. These bounds are evaluated on a benchmark made of 11 problems extracted from the PAutoma C challenge (Verwer et al., 2012). |
| Researcher Affiliation | Academia | Aix Marseille Universit e, CNRS, LIF, 13288 Marseille Cedex 9, FRANCE and Universit e Jean Monnet de Saint-Etienne, CNRS, La HC, 42000 Saint-Etienne Cedex 2, FRANCE |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. There are no explicit code release statements or repository links. |
| Open Datasets | Yes | These bounds are evaluated on a benchmark made of 11 problems extracted from the PAutoma C challenge (Verwer et al., 2012). |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. It mentions using a 'sample' for evaluation but does not specify how this sample is partitioned into distinct train, validation, and test sets for model training and evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details. It only vaguely mentions 'computing resources' without any model numbers, processor types, or memory details. |
| Software Dependencies | No | The paper mentions 'standard SVD algorithms available from Num Py or Sci Py' but does not specify version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | No | The paper focuses on theoretical bounds and their empirical validation. It does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings for a learning model. |