Search Results for author:
Found 40 papers, 21 papers with code
InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning
In this paper, we introduce InterLUDE, a new approach to enhance SSL made of two parts that each benefit from labeled-unlabeled interaction.
Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis Diagnosis
However, existing deep learning pipelines for assessing AS from echocardiograms have two key limitations.
Discovering group dynamics in synchronous time series via hierarchical recurrent switching-state models
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.
A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data
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
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.
Systematic comparison of semi-supervised and self-supervised learning for medical image classification
Yet past benchmarks do not focus on medical tasks and rarely compare self- and semi- methods together on an equal footing.
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning
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.
Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations
We consider probabilistic models for sequential observations which exhibit gradual transitions among a finite number of states.
Fix-A-Step: Semi-supervised Learning from Uncurated Unlabeled Data
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
In order for artificial agents to successfully perform tasks in changing environments, they must be able to both detect and adapt to novelty.
Easy Variational Inference for Categorical Models via an Independent Binary Approximation
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.
Dynamical Wasserstein Barycenters for Time-series Modeling
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.
A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms
Semi-supervised image classification has shown substantial progress in learning from limited labeled data, but recent advances remain largely untested for clinical applications.
Evaluating the Use of Reconstruction Error for Novelty Localization
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
In this work, we propose a new model, Stochastic Iterative Graph MAtching (SIGMA), to address the graph matching problem.
Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories
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
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.
Modeling Graph Node Correlations with Neighbor Mixture Models
We propose a new model, the Neighbor Mixture Model (NMM), for modeling node labels in a graph.
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints
We develop a new framework for learning variational autoencoders and other deep generative models that balances generative and discriminative goals.
On Matched Filtering for Statistical Change Point Detection
Non-parametric and distribution-free two-sample tests have been the foundation of many change point detection algorithms.
Enzyme promiscuity prediction using hierarchy-informed multi-label classification
Some interactions are attributed to natural selection and involve the enzyme's natural substrates.
POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning
Many medical decision-making tasks can be framed as partially observed Markov decision processes (POMDPs).
Optimal Transport Based Change Point Detection and Time Series Segment Clustering
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).
Rapid Model Comparison by Amortizing Across Models
Comparing the inferences of diverse candidate models is an essential part of model checking and escaping local optima.
Challenges in Computing and Optimizing Upper Bounds of Marginal Likelihood based on Chi-Square Divergences
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.
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization
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
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.
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 standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced.
Ranked #3 on
Length-of-Stay prediction
on MIMIC-III
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
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.
Prediction-Constrained Topic Models for Antidepressant Recommendation
Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks.
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
The lack of interpretability remains a key barrier to the adoption of deep models in many applications.
From Patches to Images: A Nonparametric Generative Model
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.
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models
Supervisory signals have the potential to make low-dimensional data representations, like those learned by mixture and topic models, more interpretable and useful.
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
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
Supervised topic models can help clinical researchers find interpretable cooccurence patterns in count data that are relevant for diagnostics.
Fast Learning of Clusters and Topics via Sparse Posteriors
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.
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models
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.
Memoized Online Variational Inference for Dirichlet Process Mixture Models
Variational inference algorithms provide the most effective framework for large-scale training of Bayesian nonparametric models.
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 modeling multiple related time series.
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data
Applications of Bayesian nonparametric methods require learning and inference algorithms which efficiently explore models of unbounded complexity.
