The Sample Complexity of Learning Linear Predictors with the Squared Loss
Authors: Ohad Shamir
JMLR 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a tight sample complexity bound for learning bounded-norm linear predictors with respect to the squared loss. Our focus is on an agnostic PAC-style setting, where no assumptions are made on the data distribution beyond boundedness. This contrasts with existing results in the literature, which rely on other distributional assumptions, refer to speciļ¬c parameter settings, or use other performance measures. |
| Researcher Affiliation | Academia | Ohad Shamir EMAIL Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot 7610001, Israel |
| Pseudocode | No | No structured pseudocode or algorithm blocks are present in the paper. The paper primarily consists of mathematical derivations and proofs for sample complexity bounds, without presenting any specific algorithms in a pseudocode format. |
| Open Source Code | No | The paper does not contain any explicit statements about making source code available, nor does it provide links to code repositories for the methodology described. |
| Open Datasets | No | The paper is theoretical, focusing on sample complexity bounds under general data distributions. It does not utilize or reference any specific public or open datasets, as it does not involve empirical evaluation. |
| Dataset Splits | No | As the paper is theoretical and does not conduct experiments on specific datasets, there is no information provided regarding dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is purely theoretical, presenting mathematical proofs and bounds. It does not describe any experimental setup or the hardware used to run experiments, as no experiments were conducted. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs. It does not mention any specific software or library dependencies with version numbers, as it does not describe any implemented experiments. |
| Experiment Setup | No | The paper is theoretical, providing mathematical analysis and proofs for sample complexity. It does not include details on experimental setup, hyperparameters, or system-level training settings, as no empirical experiments were performed. |