← Authors

Ricardo Henao

70 papers · 2009–2025 · 14 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐝 Cross-Pollinator (15) πŸƒ Academic Marathon (16) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (14) πŸ—ΊοΈ Taxonomy Completionist (18) 🐣 Hot Topic Early Bird 🌟 Keyword Trendsetter Combo (3) πŸ† Keyword Champion πŸ† Grand Slam πŸ”¬ Deep Specialist (18) 🀝 Dynamic Duo (41) πŸš€ Conference Pioneer πŸ”₯ Unstoppable (12) ⚑ Prolific Year (7) πŸ’Ž Century Club (70) πŸ—ƒοΈ Keyword Collector (297) πŸ“ˆ Trend Setter

Conferences

NIPS (16) ICML (14) AISTATS (8) EMNLP (7) ACL (5) MLHC (5) AAAI (4) NAACL (3) CVPR (2) JMLR (2) ICLR (1) IJCAI (1) UAI (1) WACV (1)

Top co-authors

Lawrence Carin (41) Zhe Gan (15) Chunyuan Li (11) Guoyin Wang (10) Rui Wang (9) Chenyang Tao (9) Yizhe Zhang (9) Liqun Chen (8) Ruiyi Zhang (8) Dinghan Shen (6)

Keywords

variational inference (8) markov chain monte carlo (6) bayesian inference (6) convolutional neural network (6) representation learning (6) text classification (5) survival analysis (5) generative model (5) variational autoencoder (5) knowledge distillation (4) causal inference (4) mutual information (4) semi-supervised learning (4) pretrained language model (4) sigmoid belief network (4) data augmentation (3) named entity recognition (3) generative adversarial network (3) text generation (3) adversarial learning (3)

Papers

Cross-Modal Imputation and Uncertainty Estimation for Spatial Transcriptomics AISTATS 2025
Generating Accurate Synthetic Survival Data by Conditioning on Outcomes MLHC 2025
Learning Survival Distributions with the Asymmetric Laplace Distribution ICML 2025
Learning to Substitute Words with Model-based Score Ranking NAACL 2025
On Understanding Attention-Based In-Context Learning for Categorical Data ICML 2025
Learning Subjective Label Distributions via Sociocultural Descriptors EMNLP 2025
Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning NAACL 2024
Adaptive Discretization for Event PredicTion (ADEPT) AISTATS 2024
Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources ICML 2024
Toward Fairness in Text Generation via Mutual Information Minimization based on Importance Sampling AISTATS 2023
An Effective Meaningful Way to Evaluate Survival Models ICML 2023
Pushing the Efficiency Limit Using Structured Sparse Convolutions WACV 2023
InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding NIPS 2023
Mitigating Test-Time Bias for Fair Image Retrieval NIPS 2023
Few-Shot Composition Learning for Image Retrieval with Prompt Tuning AAAI 2023
Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets ACL 2023
Hawkes Process with Flexible Triggering Kernels MLHC 2023
Estimating Total Correlation with Mutual Information Estimators AISTATS 2023
Gradient Importance Learning for Incomplete Observations ICLR 2022
Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data AISTATS 2022
Context-aware Information-theoretic Causal De-biasing for Interactive Sequence Labeling EMNLP 2022
Open World Classification with Adaptive Negative Samples EMNLP 2022
Efficient Classification of Very Large Images With Tiny Objects CVPR 2022
Few-Shot Class-Incremental Learning for Named Entity Recognition ACL 2022
Capturing actionable dynamics with structured latent ordinary differential equations UAI 2022
Variational Disentanglement for Rare Event Modeling AAAI 2021
SpanPredict: Extraction of Predictive Document Spans with Neural Attention NAACL 2021
Counterfactual Representation Learning with Balancing Weights AISTATS 2021
Wasserstein Contrastive Representation Distillation CVPR 2021
Unsupervised Paraphrasing Consistency Training for Low Resource Named Entity Recognition EMNLP 2021
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer NIPS 2021
Learning Autoencoders with Relational Regularization ICML 2020
Neural Conditional Event Time Models MLHC 2020
Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning AAAI 2020
Integrating Task Specific Information into Pretrained Language Models for Low Resource Fine Tuning EMNLP 2020
Improving Textual Network Learning with Variational Homophilic Embeddings NIPS 2019
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods NIPS 2019
Communication-Efficient Stochastic Gradient MCMC for Neural Networks AAAI 2019
Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images MLHC 2019
Variational Inference and Model Selection with Generalized Evidence Bounds ICML 2018
Adversarial Time-to-Event Modeling ICML 2018
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets ICML 2018
Multi-Label Learning from Medical Plain Text with Convolutional Residual Models MLHC 2018
Joint Embedding of Words and Labels for Text Classification ACL 2018
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment EMNLP 2018
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing ACL 2018
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms ACL 2018
Chi-square Generative Adversarial Network ICML 2018
Learning Generic Sentence Representations Using Convolutional Neural Networks EMNLP 2017
VAE Learning via Stein Variational Gradient Descent NIPS 2017
Deconvolutional Paragraph Representation Learning NIPS 2017
Adversarial Symmetric Variational Autoencoder NIPS 2017
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching NIPS 2017
Stochastic Gradient Monomial Gamma Sampler ICML 2017
Adversarial Feature Matching for Text Generation ICML 2017
Bayesian Dictionary Learning with Gaussian Processes and Sigmoid Belief Networks IJCAI 2016
Variational Autoencoder for Deep Learning of Images, Labels and Captions NIPS 2016
Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization AISTATS 2016
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling NIPS 2016
Electronic Health Record Analysis via Deep Poisson Factor Models JMLR 2016
A Multitask Point Process Predictive Model ICML 2015
Scalable Deep Poisson Factor Analysis for Topic Modeling ICML 2015
Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood ICML 2015
Learning Deep Sigmoid Belief Networks with Data Augmentation AISTATS 2015
Deep Poisson Factor Modeling NIPS 2015
Deep Temporal Sigmoid Belief Networks for Sequence Modeling NIPS 2015
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings NIPS 2015
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling NIPS 2014
Sparse Linear Identifiable Multivariate Modeling JMLR 2011
Bayesian Sparse Factor Models and DAGs Inference and Comparison NIPS 2009