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Creators/Authors contains: "Hiranandani, Gaurush"

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  1. ; ; ; (, Proceedings of Machine Learning Research)
    Cussens, James; Zhang, Kun (Ed.)
    Metric elicitation is a recent framework for eliciting classification performance metrics that best reflect implicit user preferences based on the task and context. However, available elicitation strategies have been limited to linear (or quasi-linear) functions of predictive rates, which can be practically restrictive for many applications including fairness. This paper develops a strategy for eliciting more flexible multiclass metrics defined by quadratic functions of rates, designed to reflect human preferences better. We show its application in eliciting quadratic violation-based group-fair metrics. Our strategy requires only relative preference feedback, is robust to noise, and achieves near-optimal query complexity. We further extend this strategy to eliciting polynomial metrics – thus broadening the use cases for metric elicitation. 
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    Accepted Manuscript Full Text Available
  2. (, Advances in neural information processing systems)
    Full Text Available 
  3. (, Proceedings of Machine Learning Research)
    Accepted Manuscript Full Text Available