Work

Structural query-by-committee

Year: 2018

Type: preprint

Abstract: In this work, we describe a framework that unifies many different interactive learning tasks. We present a generalization of the {\it query-by-committee} active learning algorithm for this setting, and we study its consistency and rate of convergence, both theoretically and empirically, with and without noise.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1803.06586

Authors: Tosh, Christopher, Dasgupta, Sanjoy

Institution: University of California San Diego

Language: English

Topic: Machine Learning and Algorithms

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Simons Institute for the Theory of Computing, University of California Berkeley

OpenAlex ID: W2789933281

Cites: 9

Cited by:

Related to: 20