Henao, Ricardo

62 publications

ICLR 2026 Learning Survival Distributions with Individually Calibrated Asymmetric Laplace Distribution Deming Sheng, Ricardo Henao

NeurIPS 2025 Coupling Generative Modeling and an Autoencoder with the Causal Bridge Ruolin Meng, Ming-Yu Chung, Dhanajit Brahma, Ricardo Henao, Lawrence Carin

AISTATS 2025 Cross-Modal Imputation and Uncertainty Estimation for Spatial Transcriptomics Xiangyu Guo, Ricardo Henao

MLHC 2025 Generating Accurate Synthetic Survival Data by Conditioning on Outcomes Mohd Ashhad, Ricardo Henao

ICML 2025 Learning Survival Distributions with the Asymmetric Laplace Distribution Deming Sheng, Ricardo Henao

ICML 2025 On Understanding Attention-Based In-Context Learning for Categorical Data Aaron T Wang, William Convertino, Xiang Cheng, Ricardo Henao, Lawrence Carin

AISTATS 2024 Adaptive Discretization for Event PredicTion (ADEPT) Jimmy Hickey, Ricardo Henao, Daniel Wojdyla, Michael Pencina, Matthew Engelhard

ICML 2024 Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources Meng Xia, Jonathan Wilson, Benjamin Goldstein, Ricardo Henao

ICML 2023 An Effective Meaningful Way to Evaluate Survival Models Shi-Ang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner

AISTATS 2023 Estimating Total Correlation with Mutual Information Estimators Ke Bai, Pengyu Cheng, Weituo Hao, Ricardo Henao, Larry Carin

AAAI 2023 Few-Shot Composition Learning for Image Retrieval with Prompt Tuning Junda Wu, Rui Wang, Handong Zhao, Ruiyi Zhang, Chaochao Lu, Shuai Li, Ricardo Henao

MLHC 2023 Hawkes Process with Flexible Triggering Kernels Yamac Isik, Paidamoyo Chapfuwa, Connor Davis, Ricardo Henao

NeurIPS 2023 InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding Junda Wu, Tong Yu, Rui Wang, Zhao Song, Ruiyi Zhang, Handong Zhao, Chaochao Lu, Shuai Li, Ricardo Henao

NeurIPS 2023 Mitigating Test-Time Bias for Fair Image Retrieval Fanjie Kong, Shuai Yuan, Weituo Hao, Ricardo Henao

WACV 2023 Pushing the Efficiency Limit Using Structured Sparse Convolutions Vinay Kumar Verma, Nikhil Mehta, Shijing Si, Ricardo Henao, Lawrence Carin

TMLR 2023 Reliable Active Learning via Influence Functions Meng Xia, Ricardo Henao

AISTATS 2023 Toward Fairness in Text Generation via Mutual Information Minimization Based on Importance Sampling Rui Wang, Pengyu Cheng, Ricardo Henao

AISTATS 2022 Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data Matthew Engelhard, Ricardo Henao

NeurIPSW 2022 A Framework for the Evaluation of Clinical Time Series Models Michael Gao, Jiayu Yao, Ricardo Henao

UAI 2022 Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations Paidamoyo Chapfuwa, Sherri Rose, Lawrence Carin, Edward Meeds, Ricardo Henao

CVPR 2022 Efficient Classification of Very Large Images with Tiny Objects Fanjie Kong, Ricardo Henao

ICLR 2022 Gradient Importance Learning for Incomplete Observations Qitong Gao, Dong Wang, Joshua David Amason, Siyang Yuan, Chenyang Tao, Ricardo Henao, Majda Hadziahmetovic, Lawrence Carin, Miroslav Pajic

AISTATS 2021 Counterfactual Representation Learning with Balancing Weights Serge Assaad, Shuxi Zeng, Chenyang Tao, Shounak Datta, Nikhil Mehta, Ricardo Henao, Fan Li, Lawrence Carin

NeurIPS 2021 Supercharging Imbalanced Data Learning with Energy-Based Contrastive Representation Transfer Junya Chen, Zidi Xiu, Benjamin Goldstein, Ricardo Henao, Lawrence Carin, Chenyang Tao

AAAI 2021 Variational Disentanglement for Rare Event Modeling Zidi Xiu, Chenyang Tao, Michael Gao, Connor Davis, Benjamin Alan Goldstein, Ricardo Henao

CVPR 2021 Wasserstein Contrastive Representation Distillation Liqun Chen, Dong Wang, Zhe Gan, Jingjing Liu, Ricardo Henao, Lawrence Carin

ICML 2020 Learning Autoencoders with Relational Regularization Hongteng Xu, Dixin Luo, Ricardo Henao, Svati Shah, Lawrence Carin

MLHC 2020 Neural Conditional Event Time Models Matthew Engelhard, Samuel Berchuck, Joshua D’Arcy, Ricardo Henao

AAAI 2020 Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning Liqun Chen, Ke Bai, Chenyang Tao, Yizhe Zhang, Guoyin Wang, Wenlin Wang, Ricardo Henao, Lawrence Carin

AAAI 2019 Communication-Efficient Stochastic Gradient MCMC for Neural Networks Chunyuan Li, Changyou Chen, Yunchen Pu, Ricardo Henao, Lawrence Carin

NeurIPS 2019 Improving Textual Network Learning with Variational Homophilic Embeddings Wenlin Wang, Chenyang Tao, Zhe Gan, Guoyin Wang, Liqun Chen, Xinyuan Zhang, Ruiyi Zhang, Qian Yang, Ricardo Henao, Lawrence Carin

NeurIPS 2019 Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods Kevin Liang, Guoyin Wang, Yitong Li, Ricardo Henao, Lawrence Carin

MLHC 2019 Thyroid Cancer Malignancy Prediction from Whole Slide Cytopathology Images David Dov, Shahar Z. Kovalsky, Jonathan Cohen, Danielle Elliott Range, Ricardo Henao, Lawrence Carin

ICML 2018 Adversarial Time-to-Event Modeling Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li, Courtney Page, Benjamin Goldstein, Lawrence Carin Duke, Ricardo Henao

ICML 2018 Chi-Square Generative Adversarial Network Chenyang Tao, Liqun Chen, Ricardo Henao, Jianfeng Feng, Lawrence Carin Duke

AAAI 2018 Deconvolutional Latent-Variable Model for Text Sequence Matching Dinghan Shen, Yizhe Zhang, Ricardo Henao, Qinliang Su, Lawrence Carin

ICML 2018 JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets Yunchen Pu, Shuyang Dai, Zhe Gan, Weiyao Wang, Guoyin Wang, Yizhe Zhang, Ricardo Henao, Lawrence Carin Duke

MLHC 2018 Multi-Label Learning from Medical Plain Text with Convolutional Residual Models Yinyuan Zhang, Ricardo Henao, Zhe Gan, Yitong Li, Lawrence Carin

ICML 2018 Variational Inference and Model Selection with Generalized Evidence Bounds Liqun Chen, Chenyang Tao, Ruiyi Zhang, Ricardo Henao, Lawrence Carin Duke

NeurIPS 2017 ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching Chunyuan Li, Hao Liu, Changyou Chen, Yuchen Pu, Liqun Chen, Ricardo Henao, Lawrence Carin

ICML 2017 Adversarial Feature Matching for Text Generation Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, Lawrence Carin

NeurIPS 2017 Adversarial Symmetric Variational Autoencoder Yuchen Pu, Weiyao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li, Lawrence Carin

NeurIPS 2017 Deconvolutional Paragraph Representation Learning Yizhe Zhang, Dinghan Shen, Guoyin Wang, Zhe Gan, Ricardo Henao, Lawrence Carin

ICML 2017 Stochastic Gradient Monomial Gamma Sampler Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin

NeurIPS 2017 VAE Learning via Stein Variational Gradient Descent Yuchen Pu, Zhe Gan, Ricardo Henao, Chunyuan Li, Shaobo Han, Lawrence Carin

IJCAI 2016 Bayesian Dictionary Learning with Gaussian Processes and Sigmoid Belief Networks Yizhe Zhang, Ricardo Henao, Chunyuan Li, Lawrence Carin

JMLR 2016 Electronic Health Record Analysis via Deep Poisson Factor Models Ricardo Henao, James T. Lu, Joseph E. Lucas, Jeffrey Ferranti, Lawrence Carin

ECML-PKDD 2016 Laplacian Hamiltonian Monte Carlo Yizhe Zhang, Changyou Chen, Ricardo Henao, Lawrence Carin

AISTATS 2016 Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization Zhao Song, Ricardo Henao, David E. Carlson, Lawrence Carin

AAAI 2016 Learning a Hybrid Architecture for Sequence Regression and Annotation Yizhe Zhang, Ricardo Henao, Lawrence Carin, Jianling Zhong, Alexander J. Hartemink

NeurIPS 2016 Towards Unifying Hamiltonian Monte Carlo and Slice Sampling Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin

NeurIPS 2016 Variational Autoencoder for Deep Learning of Images, Labels and Captions Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, Lawrence Carin

ICML 2015 A Multitask Point Process Predictive Model Wenzhao Lian, Ricardo Henao, Vinayak Rao, Joseph Lucas, Lawrence Carin

NeurIPS 2015 Deep Poisson Factor Modeling Ricardo Henao, Zhe Gan, James Lu, Lawrence Carin

NeurIPS 2015 Deep Temporal Sigmoid Belief Networks for Sequence Modeling Zhe Gan, Chunyuan Li, Ricardo Henao, David E Carlson, Lawrence Carin

NeurIPS 2015 Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings Piyush Rai, Changwei Hu, Ricardo Henao, Lawrence Carin

AISTATS 2015 Learning Deep Sigmoid Belief Networks with Data Augmentation Zhe Gan, Ricardo Henao, David E. Carlson, Lawrence Carin

ICML 2015 Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood Xin Yuan, Ricardo Henao, Ephraim Tsalik, Raymond Langley, Lawrence Carin

ICML 2015 Scalable Deep Poisson Factor Analysis for Topic Modeling Zhe Gan, Changyou Chen, Ricardo Henao, David Carlson, Lawrence Carin

NeurIPS 2014 Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling Ricardo Henao, Xin Yuan, Lawrence Carin

JMLR 2011 Sparse Linear Identifiable Multivariate Modeling Ricardo Henao, Ole Winther

NeurIPS 2009 Bayesian Sparse Factor Models and DAGs Inference and Comparison Ricardo Henao, Ole Winther