Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)
Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi O. Koyejo
Metric Elicitation is a principled framework for selecting the performance metric that best reflects implicit user preferences. However, available strategies have so far been limited to binary classification. In this paper, we propose novel strategies for eliciting multiclass classification performance metrics using only relative preference feedback. We also show that the strategies are robust to both finite sample and feedback noise.