# Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples

@article{Bhatia2021SimpleAF, title={Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples}, author={Jagdeep Bhatia}, journal={J. Mach. Learn. Res.}, year={2021}, volume={22}, pages={15:1-15:30} }

This work describes simple and efficient algorithms for interactively learning non-binary concepts in the learning from random counter-examples (LRC) model. Here, learning takes place from random counter-examples that the learner receives in response to their proper equivalence queries. In this context, the learning time is defined as the number of counter-examples needed by the learner to identify the target concept. Such learning is particularly suited for online ranking, classification…

## One Citation

Teaching via Best-Case Counterexamples in the Learning-with-Equivalence-Queries Paradigm

- Computer ScienceNeurIPS
- 2021

This paper considers the optimal teacher who picks bestcase counterexamples to teach the target hypothesis within a hypothesis class, and introduces LwEQ-TD, a notion of TD capturing the teaching complexity (i.e., the number of queries made) in this paradigm.

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