141 citations · 597 across the 22 of their papers we have counts for
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stat.ML2022
Optimality in Noisy Importance Sampling
Fernando Llorente, Luca Martino, Jesse Read +1
In this work, we analyze the noisy importance sampling (IS), i.e., IS working with noisy evaluations of the target density. We present the general framework and derive optimal prop…
stat.ML2015★ 7 cited
Multi-label Classification using Labels as Hidden Nodes
Jesse Read, Jaakko Hollmén
Competitive methods for multi-label classification typically invest in learning labels together. To do so in a beneficial way, analysis of label dependence is often seen as a funda…
stat.ML2015★ 84 cited
Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises
J. Read, L. Martino, P. Olmos +1
Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier…