
Wasserstein Contrastive Representation Distillation
The primary goal of knowledge distillation (KD) is to encapsulate the in...
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Proactive PseudoIntervention: Causally Informed Contrastive Learning For Interpretable Vision Models
Deep neural networks have shown significant promise in comprehending com...
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Supercharging Imbalanced Data Learning With Causal Representation Transfer
Dealing with severe class imbalance poses a major challenge for realwor...
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Counterfactual Representation Learning with Balancing Weights
A key to causal inference with observational data is achieving balance i...
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Variational Disentanglement for Rare Event Modeling
Combining the increasing availability and abundance of healthcare data a...
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Weakly supervised crossdomain alignment with optimal transport
Crossdomain alignment between image objects and text sequences is key t...
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Survival Analysis meets Counterfactual Inference
There is growing interest in applying machine learning methods for count...
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Neural Conditional Event Time Models
Event time models predict occurrence times of an event of interest based...
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Variational Learning of Individual Survival Distributions
The abundance of modern health data provides many opportunities for the ...
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Survival Cluster Analysis
Conventional survival analysis approaches estimate risk scores or indivi...
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MachineLearningBased Multiple Abnormality Prediction with LargeScale Chest Computed Tomography Volumes
Developing machine learning models for radiology requires largescale im...
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Learning Autoencoders with Relational Regularization
A new algorithmic framework is proposed for learning autoencoders of dat...
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KernelBased Approaches for Sequence Modeling: Connections to Neural Methods
We investigate timedependent data analysis from the perspective of recu...
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StraightThrough Estimator as Projected Wasserstein Gradient Flow
The StraightThrough (ST) estimator is a widely used technique for back...
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Improving Textual Network Learning with Variational Homophilic Embeddings
The performance of many network learning applications crucially hinges o...
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Discriminative Clustering for Robust Unsupervised Domain Adaptation
Unsupervised domain adaptation seeks to learn an invariant and discrimin...
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Survival Function Matching for Calibrated TimetoEvent Predictions
Models for predicting the time of a future event are crucial for risk as...
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A DeepLearning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images
We consider thyroidmalignancy prediction from ultrahighresolution who...
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An unsupervised transfer learning algorithm for sleep monitoring
Objective: To develop multisensorwearabledevice sleep monitoring algor...
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Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images
We consider preoperative prediction of thyroid cancer based on ultrahig...
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Improved SemanticAware Network Embedding with FineGrained Word Alignment
Network embeddings, which learn lowdimensional representations for each...
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Hierarchical infinite factor model for improving the prediction of surgical complications for geriatric patients
We develop a hierarchical infinite latent factor model (HIFM) to appropr...
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JointGAN: MultiDomain Joint Distribution Learning with Generative Adversarial Nets
A new generative adversarial network is developed for joint distribution...
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Baseline Needs More Love: On Simple WordEmbeddingBased Models and Associated Pooling Mechanisms
Many deep learning architectures have been proposed to model the composi...
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NASH: Toward EndtoEnd Neural Architecture for Generative Semantic Hashing
Semantic hashing has become a powerful paradigm for fast similarity sear...
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Joint Embedding of Words and Labels for Text Classification
Word embeddings are effective intermediate representations for capturing...
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Adversarial TimetoEvent Modeling
Modern health data science applications leverage abundant molecular and ...
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MultiLabel Learning from Medical Plain Text with Convolutional Residual Models
Predicting diagnoses from Electronic Health Records (EHRs) is an importa...
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Deconvolutional LatentVariable Model for Text Sequence Matching
A latentvariable model is introduced for text matching, inferring sente...
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ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
We investigate the nonidentifiability issues associated with bidirectio...
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Deconvolutional Paragraph Representation Learning
Learning latent representations from long text sequences is an important...
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Adversarial Feature Matching for Text Generation
The Generative Adversarial Network (GAN) has achieved great success in g...
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Stochastic Gradient Monomial Gamma Sampler
Recent advances in stochastic gradient techniques have made it possible ...
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Learning Generic Sentence Representations Using Convolutional Neural Networks
We propose a new encoderdecoder approach to learn distributed sentence ...
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Variational Autoencoder for Deep Learning of Images, Labels and Captions
A novel variational autoencoder is developed to model images, as well as...
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Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demo...
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Learning a Hybrid Architecture for Sequence Regression and Annotation
When learning a hidden Markov model (HMM), sequen tial observations can...
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Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Deep dynamic generative models are developed to learn sequential depende...
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NonGaussian Discriminative Factor Models via the MaxMargin RankLikelihood
We consider the problem of discriminative factor analysis for data that ...
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Efficient hierarchical clustering for continuous data
We present an new sequential Monte Carlo sampler for coalescent based Ba...
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Predictive Active Set Selection Methods for Gaussian Processes
We propose an active set selection framework for Gaussian process classi...
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Ricardo Henao
verfied profile
Department of Biostatistics and Bioinformatics
Department of Electrical and Computer Engineering
Duke University