
Dynamical Wasserstein Barycenters for Timeseries Modeling
Many time series can be modeled as a sequence of segments representing h...
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A New Semisupervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms
Semisupervised image classification has shown substantial progress in l...
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Evaluating the Use of Reconstruction Error for Novelty Localization
The pixelwise reconstruction error of deep autoencoders is often utilize...
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Stochastic Iterative Graph Matching
Recent works leveraging Graph Neural Networks to approach graph matching...
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Approximate Bayesian Computation for an ExplicitDuration Hidden Markov Model of COVID19 Hospital Trajectories
We address the problem of modeling constrained hospital resources in the...
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Forecasting COVID19 Counts At A Single Hospital: A Hierarchical Bayesian Approach
We consider the problem of forecasting the daily number of hospitalized ...
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Modeling Graph Node Correlations with Neighbor Mixture Models
We propose a new model, the Neighbor Mixture Model (NMM), for modeling n...
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Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints
We develop a new framework for learning variational autoencoders and oth...
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On Matched Filtering for Statistical Change Point Detection
Nonparametric and distributionfree twosample tests have been the foun...
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Hierarchical Classification of Enzyme Promiscuity Using Positive, Unlabeled, and Hard Negative Examples
Despite significant progress in sequencing technology, there are many ce...
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POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning
Many medical decisionmaking settings can be framed as partially observe...
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Optimal Transport Based Change Point Detection and Time Series Segment Clustering
Two common problems in time series analysis are the decomposition of the...
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Optimizing for Interpretability in Deep Neural Networks with Tree Regularization
Deep models have advanced prediction in many domains, but their lack of ...
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Feature Robustness in Nonstationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
When training clinical prediction models from electronic health records ...
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MIMICExtract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMICIII
Robust machine learning relies on access to data that can be used with s...
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Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
Machine learning for healthcare often trains models on deidentified dat...
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PredictionConstrained Topic Models for Antidepressant Recommendation
Supervisory signals can help topic models discover lowdimensional data ...
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Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
The lack of interpretability remains a key barrier to the adoption of de...
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PredictionConstrained Training for SemiSupervised Mixture and Topic Models
Supervisory signals have the potential to make lowdimensional data repr...
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Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Neural networks are among the most accurate supervised learning methods ...
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Fast Learning of Clusters and Topics via Sparse Posteriors
Mixture models and topic models generate each observation from a single ...
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Joint modeling of multiple time series via the beta process with application to motion capture segmentation
We propose a Bayesian nonparametric approach to the problem of jointly m...
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Michael C. Hughes
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