Active Regression by Stratification
Authors: Sivan Sabato, Remi Munos
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a new active learning algorithm for parametric linear regression with random design. We provide finite sample convergence guarantees for general distributions in the misspecified model. This is the first active learner for this setting that provably can improve over passive learning.The new active learner algorithm and its analysis are provided in Section 5, with the main result stated in Theorem 5.1. Theorem 5.1 is be proved via a series of lemmas. |
| Researcher Affiliation | Collaboration | Sivan Sabato Department of Computer Science Ben Gurion University, Beer Sheva, Israel sabatos@cs.bgu.ac.il Remi Munos INRIA Lille, France remi.munos@inria.fr Current Affiliation: Google Deep Mind. |
| Pseudocode | Yes | Algorithm 1 Active Regression input Confidence δ (0, 1), label budget m, partition A. output ˆw Rd |
| Open Source Code | No | The paper does not contain any statement about releasing source code or providing a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not specify any dataset splits (training, validation, test) for empirical data. |
| Hardware Specification | No | The paper is theoretical and does not provide any details about hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details on an experimental setup, hyperparameters, or system-level training settings. |