Ricardo Henao

Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization

2016 · International Conference on Artificial Intelligence and Statistics

Belief networks are commonly used generative models of data, but require expensive posterior estimation to train and test the model. Learning typically proceeds by posterior sampling, variational approximations, or recognition networks, combined with stochastic optimization. …

Efficient model-based clustering with coalescents: Application to multiple outcomes using medical records data

2016 · arXiv (Cornell University)

We present a sequential Monte Carlo sampler for coalescent based Bayesian hierarchical clustering. The model is appropriate for multivariate non-\iid data and our approach offers a substantial reduction in computational cost when compared to the …

Wasserstein Cross-Lingual Alignment For Named Entity Recognition

2022 · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Supervised training of Named Entity Recognition (NER) models generally require large amounts of annotations, which are hardly available for less widely used (low resource) languages, e.g., Armenian and Dutch. Therefore, it will be desirable if …

Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning

2024

In this paper, we study personalized federated learning for text classification with Pretrained Language Models (PLMs).We identify two challenges in efficiently leveraging PLMs for personalized federated learning: 1) Communication.PLMs are usually large in size, inducing …

Deconvolutional Latent-Variable Model for Text Sequence Matching

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives. To alleviate typical optimization challenges in latent-variable models for text, we employ deconvolutional networks as the sequence …

Deconvolutional Latent-Variable Model for Text Sequence Matching

2017 · arXiv (Cornell University)

A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives. To alleviate typical optimization challenges in latent-variable models for text, we employ deconvolutional networks as the sequence …

Joint Embedding of Words and Labels for Text Classification

2018

Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.

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

2018

Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, Lawrence Carin. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). …