Ricardo Henao

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Duke University

Department of Biostatistics and Bioinformatics

Department of Electrical and Computer Engineering

Duke University

Wasserstein Contrastive Representation Distillation

The primary goal of knowledge distillation (KD) is to encapsulate the in...
12/15/2020 ∙ by Liqun Chen, et al. ∙ 0 ∙ share

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Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models

Deep neural networks have shown significant promise in comprehending com...
12/06/2020 ∙ by Dong Wang, et al. ∙ 11 ∙ share

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Supercharging Imbalanced Data Learning With Causal Representation Transfer

Dealing with severe class imbalance poses a major challenge for real-wor...
11/25/2020 ∙ by Junya Chen, et al. ∙ 0 ∙ share

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Counterfactual Representation Learning with Balancing Weights

A key to causal inference with observational data is achieving balance i...
10/23/2020 ∙ by Serge Assaad, et al. ∙ 0 ∙ share

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Variational Disentanglement for Rare Event Modeling

Combining the increasing availability and abundance of healthcare data a...
09/17/2020 ∙ by Zidi Xiu, et al. ∙ 21 ∙ share

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Weakly supervised cross-domain alignment with optimal transport

Cross-domain alignment between image objects and text sequences is key t...
08/14/2020 ∙ by Siyang Yuan, et al. ∙ 9 ∙ share

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Survival Analysis meets Counterfactual Inference

There is growing interest in applying machine learning methods for count...
06/14/2020 ∙ by Paidamoyo Chapfuwa, et al. ∙ 0 ∙ share

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Neural Conditional Event Time Models

Event time models predict occurrence times of an event of interest based...
04/03/2020 ∙ by Matthew Engelhard, et al. ∙ 0 ∙ share

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Variational Learning of Individual Survival Distributions

The abundance of modern health data provides many opportunities for the ...
03/09/2020 ∙ by Zidi Xiu, et al. ∙ 0 ∙ share

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Survival Cluster Analysis

Conventional survival analysis approaches estimate risk scores or indivi...
02/29/2020 ∙ by Paidamoyo Chapfuwa, et al. ∙ 0 ∙ share

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Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes

Developing machine learning models for radiology requires large-scale im...
02/12/2020 ∙ by Rachel Lea Ballantyne Draelos, et al. ∙ 0 ∙ share

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Learning Autoencoders with Relational Regularization

A new algorithmic framework is proposed for learning autoencoders of dat...
02/07/2020 ∙ by Hongteng Xu, et al. ∙ 0 ∙ share

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Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods

We investigate time-dependent data analysis from the perspective of recu...
10/09/2019 ∙ by Kevin J Liang, et al. ∙ 0 ∙ share

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Straight-Through Estimator as Projected Wasserstein Gradient Flow

The Straight-Through (ST) estimator is a widely used technique for back-...
10/05/2019 ∙ by Pengyu Cheng, et al. ∙ 5 ∙ share

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Improving Textual Network Learning with Variational Homophilic Embeddings

The performance of many network learning applications crucially hinges o...
09/30/2019 ∙ by Wenlin Wang, et al. ∙ 0 ∙ share

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Discriminative Clustering for Robust Unsupervised Domain Adaptation

Unsupervised domain adaptation seeks to learn an invariant and discrimin...
05/30/2019 ∙ by Rui Wang, et al. ∙ 0 ∙ share

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Survival Function Matching for Calibrated Time-to-Event Predictions

Models for predicting the time of a future event are crucial for risk as...
05/21/2019 ∙ by Paidamoyo Chapfuwa, et al. ∙ 10 ∙ share

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A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images

We consider thyroid-malignancy prediction from ultra-high-resolution who...
04/26/2019 ∙ by David Dov, et al. ∙ 12 ∙ share

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An unsupervised transfer learning algorithm for sleep monitoring

Objective: To develop multisensor-wearable-device sleep monitoring algor...
04/07/2019 ∙ by Xichen She, et al. ∙ 0 ∙ share

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Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images

We consider preoperative prediction of thyroid cancer based on ultra-hig...
03/29/2019 ∙ by David Dov, et al. ∙ 4 ∙ share

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Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment

Network embeddings, which learn low-dimensional representations for each...
08/29/2018 ∙ by Dinghan Shen, et al. ∙ 0 ∙ share

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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...
07/24/2018 ∙ by Elizabeth Lorenzi, et al. ∙ 0 ∙ share

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JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets

A new generative adversarial network is developed for joint distribution...
06/08/2018 ∙ by Yunchen Pu, et al. ∙ 0 ∙ share

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Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

Many deep learning architectures have been proposed to model the composi...
05/24/2018 ∙ by Dinghan Shen, et al. ∙ 0 ∙ share

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NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing

Semantic hashing has become a powerful paradigm for fast similarity sear...
05/14/2018 ∙ by Dinghan Shen, et al. ∙ 0 ∙ share

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Joint Embedding of Words and Labels for Text Classification

Word embeddings are effective intermediate representations for capturing...
05/10/2018 ∙ by Guoyin Wang, et al. ∙ 0 ∙ share

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Adversarial Time-to-Event Modeling

Modern health data science applications leverage abundant molecular and ...
04/09/2018 ∙ by Paidamoyo Chapfuwa, et al. ∙ 0 ∙ share

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Multi-Label Learning from Medical Plain Text with Convolutional Residual Models

Predicting diagnoses from Electronic Health Records (EHRs) is an importa...
01/15/2018 ∙ by Xinyuan Zhang, et al. ∙ 0 ∙ share

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Deconvolutional Latent-Variable Model for Text Sequence Matching

A latent-variable model is introduced for text matching, inferring sente...
09/21/2017 ∙ by Dinghan Shen, et al. ∙ 0 ∙ share

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ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

We investigate the non-identifiability issues associated with bidirectio...
09/05/2017 ∙ by Chunyuan Li, et al. ∙ 0 ∙ share

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Deconvolutional Paragraph Representation Learning

Learning latent representations from long text sequences is an important...
08/16/2017 ∙ by Yizhe Zhang, et al. ∙ 0 ∙ share

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Adversarial Feature Matching for Text Generation

The Generative Adversarial Network (GAN) has achieved great success in g...
06/12/2017 ∙ by Yizhe Zhang, et al. ∙ 0 ∙ share

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Stochastic Gradient Monomial Gamma Sampler

Recent advances in stochastic gradient techniques have made it possible ...
06/05/2017 ∙ by Yizhe Zhang, et al. ∙ 0 ∙ share

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Learning Generic Sentence Representations Using Convolutional Neural Networks

We propose a new encoder-decoder approach to learn distributed sentence ...
11/23/2016 ∙ by Zhe Gan, et al. ∙ 0 ∙ share

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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...
09/28/2016 ∙ by Yunchen Pu, et al. ∙ 0 ∙ share

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Towards Unifying Hamiltonian Monte Carlo and Slice Sampling

We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demo...
02/25/2016 ∙ by Yizhe Zhang, et al. ∙ 0 ∙ share

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Learning a Hybrid Architecture for Sequence Regression and Annotation

When learning a hidden Markov model (HMM), sequen- tial observations can...
12/16/2015 ∙ by Yizhe Zhang, et al. ∙ 0 ∙ share

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Deep Temporal Sigmoid Belief Networks for Sequence Modeling

Deep dynamic generative models are developed to learn sequential depende...
09/23/2015 ∙ by Zhe Gan, et al. ∙ 0 ∙ share

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Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood

We consider the problem of discriminative factor analysis for data that ...
04/28/2015 ∙ by Xin Yuan, et al. ∙ 0 ∙ share

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Efficient hierarchical clustering for continuous data

We present an new sequential Monte Carlo sampler for coalescent based Ba...
04/20/2012 ∙ by Ricardo Henao, et al. ∙ 0 ∙ share

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Predictive Active Set Selection Methods for Gaussian Processes

We propose an active set selection framework for Gaussian process classi...
02/22/2011 ∙ by Ricardo Henao, et al. ∙ 0 ∙ share

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Ricardo Henao

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