3 citations · 12 across the 16 of their papers we have counts for
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Online Diverse Learning to Rank from Partial-Click Feedback
Prakhar Gupta, Gaurush Hiranandani, Harvineet Singh +3
Learning to rank is an important problem in machine learning and recommender systems. In a recommender system, a user is typically recommended a list of items. Since the user is un…
Clustered Monotone Transforms for Rating Factorization
Gaurush Hiranandani, Raghav Somani, Oluwasanmi Koyejo +1
Exploiting low-rank structure of the user-item rating matrix has been the crux of many recommendation engines. However, existing recommendation engines force raters with heterogene…
Performance Metric Elicitation from Pairwise Classifier Comparisons
Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta +1
Given a binary prediction problem, which performance metric should the classifier optimize? We address this question by formalizing the problem of Metric Elicitation. The goal of m…