Semi-supervised Active Linear Regression
Authors: Nived Rajaraman, Fnu Devvrit, Pranjal Awasthi
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we introduce an instance dependent parameter called the reduced rank, denoted RX, and propose an efficient algorithm with query complexity O(RX/ϵ). This result directly implies improved upper bounds for two important special cases: (i) active ridge regression, and (ii) active kernel ridge regression... Finally, we introduce a distributional version of the problem... here, for every X, we prove a matching instancewise lower bound of Ω(RX/ϵ) on the query complexity of any algorithm. |
| Researcher Affiliation | Collaboration | Fnu Devvrit Department of Computer Science University of Texas at Austin devvrit@cs.utexas.edu Nived Rajaraman Department of Electrical Engineering and Computer Sciences University of California, Berkeley nived.rajaraman@berkeley.edu Pranjal Awasthi Google Research & Department of Computer Science Rutgers University pranjal.awasthi@rutgers.edu |
| Pseudocode | Yes | Algorithm 1: ASURA (Active semi-SUpervised Regression Algorithm)... Algorithm 2: ϵ-well balanced sampling procedure for SSAR |
| Open Source Code | No | The paper is theoretical and does not mention the release of open-source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper focuses on theoretical analysis and algorithm design and does not describe experiments performed on specific publicly available datasets or provide access information for any dataset. |
| Dataset Splits | No | The paper does not conduct empirical studies, therefore, there is no mention of training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, therefore, no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper focuses on theoretical contributions and does not specify software dependencies with version numbers required for reproducing experimental results. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |