Search Results for author: Michael C. Hughes

Found 27 papers, 13 papers with code

Dynamical Wasserstein Barycenters for Time-series Modeling

1 code implementation13 Oct 2021 Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller

We propose a dynamical Wasserstein barycentric (DWB) model that estimates the system state over time as well as the data-generating distributions of pure states in an unsupervised manner.

Time Series

A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms

1 code implementation30 Jul 2021 Zhe Huang, Gary Long, Benjamin Wessler, Michael C. Hughes

Semi-supervised image classification has shown substantial progress in learning from limited labeled data, but recent advances remain largely untested for clinical applications.

Classification Semi-Supervised Image Classification

Evaluating the Use of Reconstruction Error for Novelty Localization

no code implementations28 Jul 2021 Patrick Feeney, Michael C. Hughes

The pixelwise reconstruction error of deep autoencoders is often utilized for image novelty detection and localization under the assumption that pixels with high error indicate which parts of the input image are unfamiliar and therefore likely to be novel.

Stochastic Iterative Graph Matching

1 code implementation4 Jun 2021 Linfeng Liu, Michael C. Hughes, Soha Hassoun, Li-Ping Liu

In this work, we propose a new model, Stochastic Iterative Graph MAtching (SIGMA), to address the graph matching problem.

Graph Matching Stochastic Optimization

Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories

1 code implementation28 Apr 2021 Gian Marco Visani, Alexandra Hope Lee, Cuong Nguyen, David M. Kent, John B. Wong, Joshua T. Cohen, Michael C. Hughes

We develop an Approximate Bayesian Computation approach that draws samples from the posterior distribution over the model's transition and duration parameters given aggregate counts from a specific location, thus adapting the model to a region or individual hospital site of interest.

Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach

1 code implementation14 Apr 2021 Alexandra Hope Lee, Panagiotis Lymperopoulos, Joshua T. Cohen, John B. Wong, Michael C. Hughes

We consider the problem of forecasting the daily number of hospitalized COVID-19 patients at a single hospital site, in order to help administrators with logistics and planning.

Time Series

Modeling Graph Node Correlations with Neighbor Mixture Models

no code implementations29 Mar 2021 Linfeng Liu, Michael C. Hughes, Li-Ping Liu

We propose a new model, the Neighbor Mixture Model (NMM), for modeling node labels in a graph.

Image Denoising Link Prediction +2

Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints

no code implementations12 Dec 2020 Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C. Hughes, Erik B. Sudderth

We develop a new framework for learning variational autoencoders and other deep generative models that balances generative and discriminative goals.

Classification General Classification +1

On Matched Filtering for Statistical Change Point Detection

no code implementations9 Jun 2020 Kevin C. Cheng, Eric L. Miller, Michael C. Hughes, Shuchin Aeron

Non-parametric and distribution-free two-sample tests have been the foundation of many change point detection algorithms.

Activity Recognition Change Point Detection

POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning

1 code implementation13 Jan 2020 Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez

Many medical decision-making tasks can be framed as partially observed Markov decision processes (POMDPs).

Decision Making

Optimal Transport Based Change Point Detection and Time Series Segment Clustering

no code implementations4 Nov 2019 Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Erika Hussey, Eric L. Miller

Two common problems in time series analysis are the decomposition of the data stream into disjoint segments that are each in some sense "homogeneous" - a problem known as Change Point Detection (CPD) - and the grouping of similar nonadjacent segments, a problem that we call Time Series Segment Clustering (TSSC).

Change Point Detection Time Series +1

Optimizing for Interpretability in Deep Neural Networks with Tree Regularization

no code implementations14 Aug 2019 Mike Wu, Sonali Parbhoo, Michael C. Hughes, Volker Roth, Finale Doshi-Velez

Moreover, for situations in which a single, global tree is a poor estimator, we introduce a regional tree regularizer that encourages the deep model to resemble a compact, axis-aligned decision tree in predefined, human-interpretable contexts.

Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks

1 code implementation2 Aug 2019 Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi

When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve.

De-identification Length-of-Stay prediction +1

MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III

2 code implementations19 Jul 2019 Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Michael C. Hughes, Tristan Naumann, Marzyeh Ghassemi

Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced.

Length-of-Stay prediction Outlier Detection +1

Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation

no code implementations30 Nov 2018 Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi

Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time.

Mortality Prediction

Prediction-Constrained Topic Models for Antidepressant Recommendation

no code implementations1 Dec 2017 Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy, Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez

Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks.

Topic Models

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

2 code implementations16 Nov 2017 Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez

The lack of interpretability remains a key barrier to the adoption of deep models in many applications.

Time Series

From Patches to Images: A Nonparametric Generative Model

1 code implementation ICML 2017 Geng Ji, Michael C. Hughes, Erik B. Sudderth

Our model is based on a novel, variational interpretation of the popular expected patch log-likelihood (EPLL) method as a model for randomly positioned grids of image patches.

Denoising Image Inpainting +1

Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models

no code implementations23 Jul 2017 Michael C. Hughes, Leah Weiner, Gabriel Hope, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez

Supervisory signals have the potential to make low-dimensional data representations, like those learned by mixture and topic models, more interpretable and useful.

Latent Variable Models Sentiment Analysis +1

Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

1 code implementation10 Mar 2017 Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez

Neural networks are among the most accurate supervised learning methods in use today, but their opacity makes them difficult to trust in critical applications, especially when conditions in training differ from those in test.

Supervised topic models for clinical interpretability

no code implementations6 Dec 2016 Michael C. Hughes, Huseyin Melih Elibol, Thomas McCoy, Roy Perlis, Finale Doshi-Velez

Supervised topic models can help clinical researchers find interpretable cooccurence patterns in count data that are relevant for diagnostics.

Topic Models

Fast Learning of Clusters and Topics via Sparse Posteriors

no code implementations23 Sep 2016 Michael C. Hughes, Erik B. Sudderth

Mixture models and topic models generate each observation from a single cluster, but standard variational posteriors for each observation assign positive probability to all possible clusters.

Topic Models

Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models

1 code implementation NeurIPS 2015 Michael C. Hughes, William T. Stephenson, Erik Sudderth

Bayesian nonparametric hidden Markov models are typically learned via fixed truncations of the infinite state space or local Monte Carlo proposals that make small changes to the state space.

Motion Capture Speaker Diarization

Memoized Online Variational Inference for Dirichlet Process Mixture Models

no code implementations NeurIPS 2013 Michael C. Hughes, Erik Sudderth

Variational inference algorithms provide the most effective framework for large-scale training of Bayesian nonparametric models.

Denoising Image Clustering +1

Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data

no code implementations NeurIPS 2012 Michael C. Hughes, Emily Fox, Erik B. Sudderth

Applications of Bayesian nonparametric methods require learning and inference algorithms which efficiently explore models of unbounded complexity.

Motion Capture Time Series

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