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07/18/2023
Accuracy versus time frontiers of semi-supervised and self-supervised learning on medical images
For many applications of classifiers to medical images, a trustworthy la...
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10/04/2022
Non-Parametric and Regularized Dynamical Wasserstein Barycenters for Time-Series Analysis
We consider probabilistic time-series models for systems that gradually ...
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08/25/2022
Fix-A-Step: Effective Semi-supervised Learning from Uncurated Unlabeled Sets
Semi-supervised learning (SSL) promises gains in accuracy compared to tr...
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06/23/2022
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds
In order for artificial agents to perform useful tasks in changing envir...
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05/31/2022
Easy Variational Inference for Categorical Models via an Independent Binary Approximation
We pursue tractable Bayesian analysis of generalized linear models (GLMs...
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10/13/2021
Dynamical Wasserstein Barycenters for Time-series Modeling
Many time series can be modeled as a sequence of segments representing h...
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07/30/2021
A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms
Semi-supervised image classification has shown substantial progress in l...
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07/28/2021
Evaluating the Use of Reconstruction Error for Novelty Localization
The pixelwise reconstruction error of deep autoencoders is often utilize...
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06/04/2021
Stochastic Iterative Graph Matching
Recent works leveraging Graph Neural Networks to approach graph matching...
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04/28/2021
Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories
We address the problem of modeling constrained hospital resources in the...
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04/14/2021
Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach
We consider the problem of forecasting the daily number of hospitalized ...
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03/29/2021
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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12/12/2020
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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06/09/2020
On Matched Filtering for Statistical Change Point Detection
Non-parametric and distribution-free two-sample tests have been the foun...
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02/18/2020
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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01/13/2020
POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning
Many medical decision-making settings can be framed as partially observe...
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11/04/2019
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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08/14/2019
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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08/02/2019
Feature Robustness in Non-stationary 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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07/19/2019
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
Robust machine learning relies on access to data that can be used with s...
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11/30/2018
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
Machine learning for healthcare often trains models on de-identified dat...
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12/01/2017
Prediction-Constrained Topic Models for Antidepressant Recommendation
Supervisory signals can help topic models discover low-dimensional data ...
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11/16/2017
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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07/23/2017
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models
Supervisory signals have the potential to make low-dimensional data repr...
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03/10/2017
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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09/23/2016
Fast Learning of Clusters and Topics via Sparse Posteriors
Mixture models and topic models generate each observation from a single ...
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08/22/2013