173 citations · 600 across the 40 of their papers we have counts for
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stat.ML2010★ 2 cited
Calibrated Surrogate Losses for Classification with Label-Dependent Costs
Clayton Scott
We present surrogate regret bounds for arbitrary surrogate losses in the context of binary classification with label-dependent costs. Such bounds relate a classifier's risk, assess…
stat.ML2010★ 1 cited
Query Learning with Exponential Query Costs
Gowtham Bellala, Suresh Bhavnani, Clayton Scott
In query learning, the goal is to identify an unknown object while minimizing the number of "yes" or "no" questions (queries) posed about that object. A well-studied algorithm for…