A Unified Framework for Consistency of Regularized Loss Minimizers
Authors: Jean Honorio, Tommi Jaakkola
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We characterize a family of regularized loss minimization problems that satisfy three properties: scaled uniform convergence, super-norm regularization, and norm-loss monotonicity. We show several theoretical guarantees within this framework, including loss consistency, norm consistency, sparsistency (i.e. support recovery) as well as sign consistency. A number of regularization problems can be shown to fall within our framework and we provide several examples. Our results can be seen as a concise summary of existing guarantees but we also extend them to new settings. |
| Researcher Affiliation | Academia | Jean Honorio JHONORIO@CSAIL.MIT.EDU Tommi Jaakkola TOMMI@CSAIL.MIT.EDU CSAIL, MIT, Cambridge, MA 02139, USA |
| Pseudocode | No | The paper contains theorems and mathematical derivations but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. There is no mention of a repository link, explicit code release statement, or code in supplementary materials. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with datasets for training. It mentions examples of problems (e.g., exponential family distributions) but does not use specific publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments requiring dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or version numbers needed to replicate experiments. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |