Improved PAC-Bayesian Bounds for Linear Regression
Authors: Vera Shalaeva, Alireza Fakhrizadeh Esfahani, Pascal Germain, Mihaly Petreczky5660-5667
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we improve the PAC-Bayesian error bound for linear regression derived in Germain et al. (2016). The improvements are two-fold. First, the proposed error bound is tighter, and converges to the generalization loss with a well-chosen temperature parameter. Second, the error bound also holds for training data that are not independently sampled. In particular, the error bound applies to certain time series generated by well-known classes of dynamical models, such as ARX models. |
| Researcher Affiliation | Academia | 1The MODAL project-team, INRIA Lille Nord-Europe, Villeneuve d Ascq, France 2Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRISt AL Centre de Recherche en Informatique Signal et Automatique de Lille, France |
| Pseudocode | No | The paper does not contain STRUCTURED PSEUDOCODE OR ALGORITHM BLOCKS (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide CONCRETE ACCESS TO SOURCE CODE (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not use or provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper is theoretical and does not provide specific dataset split information. |
| Hardware Specification | No | The paper is theoretical and does not provide specific hardware details for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not contain specific experimental setup details. |