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Initializing Services in Interactive ML Systems for Diverse Users
(ii) Suboptimal Local Solutions: The total loss (sum of loss functions across all users and all services) landscape is not convex even if the individual losses on a single service are convex, making it likely for the learning dynamics to get stuck in local minima.
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification
For such applications, a common approach is to organize these labels into a tree, enabling training and inference times that are logarithmic in the number of labels.
The Power of Batching in Multiple Hypothesis Testing
One important partition of algorithms for controlling the false discovery rate (FDR) in multiple testing is into offline and online algorithms.
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