Ricardo Henao

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