Active Learning Using Smooth Relative Regret Approximations with Applications
Authors: Nir Ailon, Ron Begleiter, Esther Ezra
JMLR 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a different tool for pool based active learning which follows from the existence of a certain uniform version of low disagreement coefficient, but is not equivalent to it. In fact, we present two fundamental active learning problems of significant interest for which our approach allows nontrivial active learning bounds. However, any general purpose method relying on the disagreement coefficient bounds only, fails to guarantee any useful bounds for these problems. The tool we use is based on the learner s ability to compute an estimator of the difference between the loss of any hypothesis and some fixed pivotal hypothesis... We prove that such an estimator implies the existence of a learning algorithm which, at each iteration, reduces its in-class excess risk to within a constant factor. |
| Researcher Affiliation | Academia | Nir Ailon EMAIL Ron Begleiter EMAIL Department of Computer Science Taub Building Technion Israel Institute of Technology Haifa 32000, Israel Esther Ezra EMAIL Coutrant Institute of Mathematical Science New York University 251 Mercer Street New York, NY, 10012 USA |
| Pseudocode | Yes | Algorithm 1 An Active Learning Algorithm from SRRA s Algorithm 2 SRRA for LRPP Algorithm 3 SRRA for Semi-Supervised k-Clustering |
| Open Source Code | No | The paper does not provide any explicit statements about releasing code, a link to a code repository, or mention of code in supplementary materials. |
| Open Datasets | No | This paper is theoretical in nature and does not conduct experiments on specific datasets. It defines abstract instance and label spaces for its theoretical analysis, but does not provide access information for any concrete, publicly available datasets. |
| Dataset Splits | No | This paper is theoretical in nature and does not conduct experiments on specific datasets. Therefore, there is no mention of training/test/validation splits. |
| Hardware Specification | No | This paper is theoretical and focuses on algorithm design and analysis of query complexity. It does not describe any experimental setup or hardware used. |
| Software Dependencies | No | This paper is theoretical and focuses on algorithm design and analysis. It does not provide specific software dependencies or version numbers for its methodology. |
| Experiment Setup | No | This paper is theoretical and primarily deals with the design and analysis of active learning algorithms and their query complexities. It does not describe an experimental setup with hyperparameters or training configurations. |