36 citations · 38 across the 4 of their papers we have counts for
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stat.ML2022
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learning
Kevin Miller, Jeff Calder
We show that uncertainty sampling is sufficient to achieve exploration versus exploitation in graph-based active learning, as long as the measure of uncertainty properly aligns wit…
stat.ML2020★ 1 cited
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations
Kevin Miller, Hao Li, Andrea L. Bertozzi
We present a novel adaptation of active learning to graph-based semi-supervised learning (SSL) under non-Gaussian Bayesian models. We present an approximation of non-Gaussian distr…
stat.ML2017★ 36 cited
Forward Thinking: Building Deep Random Forests
Kevin Miller, Chris Hettinger, Jeffrey Humpherys +2
The success of deep neural networks has inspired many to wonder whether other learners could benefit from deep, layered architectures. We present a general framework called forward…