Random features models: a way to study the success of naive imputation
Authors: Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper completes the picture for linear predictors by confirming the intuition that the bias is negligible and that surprisingly naive imputation also remains relevant in very low dimension. To this aim, we consider a unique underlying random features model, which offers a rigorous framework for studying predictive performances, whilst the dimension of the observed features varies. Building on these theoretical results, we establish finite-sample bounds on stochastic gradient (SGD) predictors applied to zero-imputed data, a strategy particularly well suited for large-scale learning. |
| Researcher Affiliation | Academia | 1Sorbonne Universit e, CNRS, Laboratoire de Probabilit es, Statistique et Mod elisation (LPSM), F-75005 Paris, France 2Institut Universitaire de France (IUF) 3CMAP, UMR7641, Ecole Polytechnique, IP Paris, 91128 Palaiseau, France. |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | No statement or link indicating the release of source code for the methodology was found. |
| Open Datasets | No | The paper focuses on theoretical analysis of models and does not use empirical datasets requiring public access information. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splitting for empirical validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup with hyperparameters or training configurations. |