36 citations · 63 across the 12 of their papers we have counts for
12 papers
MALADY: Multiclass Active Learning with Auction Dynamics on Graphs
Gokul Bhusal, Kevin Miller, Ekaterina Merkurjev
Active learning enhances the performance of machine learning methods, particularly in semi-supervised cases, by judiciously selecting a limited number of unlabeled data points for…
Dirichlet Active Learning
Kevin Miller, Ryan Murray
This work introduces Dirichlet Active Learning (DiAL), a Bayesian-inspired approach to the design of active learning algorithms. Our framework models feature-conditional class prob…
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets
James Chapman, Bohan Chen, Zheng Tan +3
Active learning improves the performance of machine learning methods by judiciously selecting a limited number of unlabeled data points to query for labels, with the aim of maximal…
Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering
Yijun Dong, Kevin Miller, Qi Lei +1
Despite the empirical success and practical significance of (relational) knowledge distillation that matches (the relations of) features between teacher and student models, the cor…
Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images
Bohan Chen, Kevin Miller, Andrea L. Bertozzi +1
We develop a graph active learning pipeline (GAP) to detect surface water and in-river sediment pixels in satellite images. The active learning approach is applied within the train…
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…
Graph-based Active Learning for Semi-supervised Classification of SAR Data
Kevin Miller, John Mauro, Jason Setiadi +4
We present a novel method for classification of Synthetic Aperture Radar (SAR) data by combining ideas from graph-based learning and neural network methods within an active learnin…
Efficient and Reliable Overlay Networks for Decentralized Federated Learning
Yifan Hua, Kevin Miller, Andrea L. Bertozzi +2
We propose near-optimal overlay networks based on -regular expander graphs to accelerate decentralized federated learning (DFL) and improve its generalization. In DFL a massive…
Model-Change Active Learning in Graph-Based Semi-Supervised Learning
Kevin Miller, Andrea L. Bertozzi
Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier. A challenge is to…
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…
Posterior Consistency of Semi-Supervised Regression on Graphs
Andrea L. Bertozzi, Bamdad Hosseini, Hao Li +2
Graph-based semi-supervised regression (SSR) is the problem of estimating the value of a function on a weighted graph from its values (labels) on a small subset of the vertices. Th…
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…