Lalit Jain

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Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algorithms to benchmark datasets, as is the norm in (passive)(More)
The goal of ordinal embedding is to represent items as points in a low-dimensional Euclidean space given a set of constraints in the form of distance comparisons like " item i is closer to item j than item k ". Ordinal constraints like this often come from human judgments. To account for errors and variation in judgments, we consider the noisy situation in(More)
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