Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Improved PAC-Bayesian Bounds for Linear Regression
Authors: Vera Shalaeva, Alireza Fakhrizadeh Esfahani, Pascal Germain, Mihaly Petreczky5660-5667
AAAI 2020 | Venue PDF | 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. |