Check the preview of 2nd version of this platform
being developed by the open MLCommons taskforce on automation and reproducibility
as a free, open-source and technology-agnostic on-prem platform.
Search Improves Label for Active Learning
lib:5ad4aa4b55942a34 (v1.0.0)
Authors: Alina Beygelzimer,Daniel Hsu,John Langford,Chicheng Zhang
Where published: NeurIPS 2016 12
ArXiv: 1602.07265
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1602.07265v2
We investigate active learning with access to two distinct oracles: Label (which is standard) and Search (which is not). The Search oracle models the situation where a human searches a database to seed or counterexample an existing solution. Search is stronger than Label while being natural to implement in many situations. We show that an algorithm using both oracles can provide exponentially large problem-dependent improvements over Label alone.
Where published: NeurIPS 2016 12
ArXiv: 1602.07265
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1602.07265v2
We investigate active learning with access to two distinct oracles: Label (which is standard) and Search (which is not). The Search oracle models the situation where a human searches a database to seed or counterexample an existing solution. Search is stronger than Label while being natural to implement in many situations. We show that an algorithm using both oracles can provide exponentially large problem-dependent improvements over Label alone.
Relevant initiatives
Related knowledge about this paper
Reproduced results (crowd-benchmarking and competitions)
Artifact and reproducibility checklists
- ACM/cTuning artifact appendix and reproducibility checklist
- NeurIPS reproducibility checklist
- SIGPLAN's checklist for empirical evaluation
- Collective Knowledge (organizing research projects based on FAIR principles)