Search Results for author: Michael C. Hughes

Found 40 papers, 21 papers with code

InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning

no code implementations15 Mar 2024 Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes

In this paper, we introduce InterLUDE, a new approach to enhance SSL made of two parts that each benefit from labeled-unlabeled interaction.

Image Classification Representation Learning

Discovering group dynamics in synchronous time series via hierarchical recurrent switching-state models

no code implementations26 Jan 2024 Michael Wojnowicz, Preetish Rath, Eric Miller, Jeffrey Miller, Clifford Hancock, Meghan O'Donovan, Seth Elkin-Frankston, Thaddeus Brunye, Michael C. Hughes

Our hierarchical switching recurrent dynamical models can be learned via closed-form variational coordinate ascent updates to all latent chains that scale linearly in the number of individual time series.

Time Series

A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data

1 code implementation29 Nov 2023 Ethan Harvey, Wansu Chen, David M. Kent, Michael C. Hughes

Practitioners building classifiers often start with a smaller pilot dataset and plan to grow to larger data in the near future.

SINCERE: Supervised Information Noise-Contrastive Estimation REvisited

1 code implementation25 Sep 2023 Patrick Feeney, Michael C. Hughes

The information noise-contrastive estimation (InfoNCE) loss function provides the basis of many self-supervised deep learning methods due to its strong empirical results and theoretic motivation.

Transfer Learning

Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning

1 code implementation25 May 2023 Zhe Huang, Benjamin S. Wessler, Michael C. Hughes

To automate screening for AS, deep networks must learn to mimic a human expert's ability to identify views of the aortic valve then aggregate across these relevant images to produce a study-level diagnosis.

Contrastive Learning Multiple Instance Learning

Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations

no code implementations4 Oct 2022 Kevin C. Cheng, Shuchin Aeron, Michael C. Hughes, Eric L. Miller

We consider probabilistic models for sequential observations which exhibit gradual transitions among a finite number of states.

Time Series Time Series Analysis

Fix-A-Step: Semi-supervised Learning from Uncurated Unlabeled Data

1 code implementation25 Aug 2022 Zhe Huang, Mary-Joy Sidhom, Benjamin S. Wessler, Michael C. Hughes

Semi-supervised learning (SSL) promises improved accuracy compared to training classifiers on small labeled datasets by also training on many unlabeled images.

NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds

2 code implementations23 Jun 2022 Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Li-Ping Liu, Matthias Scheutz, Michael C. Hughes

In order for artificial agents to successfully perform tasks in changing environments, they must be able to both detect and adapt to novelty.

Novelty Detection

Easy Variational Inference for Categorical Models via an Independent Binary Approximation

1 code implementation31 May 2022 Michael T. Wojnowicz, Shuchin Aeron, Eric L. Miller, Michael C. Hughes

This approximation makes inference straightforward and fast; using well-known auxiliary variables for probit or logistic regression, the product of binary models admits conjugate closed-form variational inference that is embarrassingly parallel across categories and invariant to category ordering.

Variational Inference

Dynamical Wasserstein Barycenters for Time-series Modeling

1 code implementation NeurIPS 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 Time Series Analysis

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.

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.

Novelty Detection

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 Time Series Analysis

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.

General Classification Image Classification

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

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 Clustering +2

Rapid Model Comparison by Amortizing Across Models

1 code implementation pproximateinference AABI Symposium 2019 Lily H. Zhang, Michael C. Hughes

Comparing the inferences of diverse candidate models is an essential part of model checking and escaping local optima.

Topic Models Variational Inference

Challenges in Computing and Optimizing Upper Bounds of Marginal Likelihood based on Chi-Square Divergences

no code implementations pproximateinference AABI Symposium 2019 Melanie F. Pradier, Michael C. Hughes, Finale Doshi-Velez

Variational inference based on chi-square divergence minimization (CHIVI) provides a way to approximate a model's posterior while obtaining an upper bound on the marginal likelihood.

Variational Inference

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.

BIG-bench Machine Learning Length-of-Stay prediction +3

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.

BIG-bench Machine Learning 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

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.

Sentiment Analysis Topic Models

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.

speaker-diarization 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.

Clustering Denoising +2

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.

Time Series Time Series Analysis

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