371 citations · 1.3k across the 68 of their papers we have counts for
71 papers
SIDEKICK: A Semantically Integrated Resource for Drug Effects, Indications, and Contraindications
Mohammad Ashhad, Olga Mashkova, Ricardo Henao +1
Pharmacovigilance and clinical decision support systems utilize structured drug safety data to guide medical practice. However, existing datasets frequently depend on terminologies…
Learning Survival Distributions with the Asymmetric Laplace Distribution
Deming Sheng, Ricardo Henao
Probabilistic survival analysis models seek to estimate the distribution of the future occurrence (time) of an event given a set of covariates. In recent years, these models have p…
Learning to Substitute Words with Model-based Score Ranking
Hongye Liu, Ricardo Henao
Smart word substitution aims to enhance sentence quality by improving word choices; however current benchmarks rely on human-labeled data. Since word choices are inherently subject…
Generating Accurate Synthetic Survival Data by Conditioning on Outcomes
Mohd Ashhad, Ricardo Henao
Synthetically generated data can improve privacy, fairness, and data accessibility; however, it can be challenging in specialized scenarios such as survival analysis. One key chall…
On Understanding Attention-Based In-Context Learning for Categorical Data
Aaron T. Wang, William Convertino, Xiang Cheng +2
In-context learning based on attention models is examined for data with categorical outcomes, with inference in such models viewed from the perspective of functional gradient desce…
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences
Yuetan Chu, Gongning Luo, Longxi Zhou +13
Pulmonary artery-vein segmentation is crucial for disease diagnosis and surgical planning and is traditionally achieved by Computed Tomography Pulmonary Angiography (CTPA). However…
Improving Event Time Prediction by Learning to Partition the Event Time Space
Jimmy Hickey, Ricardo Henao, Daniel Wojdyla +2
Recently developed survival analysis methods improve upon existing approaches by predicting the probability of event occurrence in each of a number pre-specified (discrete) time in…
InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding
Junda Wu, Tong Yu, Rui Wang +6
Soft prompt tuning achieves superior performances across a wide range of few-shot tasks. However, the performances of prompt tuning can be highly sensitive to the initialization of…
An Effective Meaningful Way to Evaluate Survival Models
Shi-ang Qi, Neeraj Kumar, Mahtab Farrokh +5
One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) -- the average of the absolute difference between the time predicted by…
Mitigating Test-Time Bias for Fair Image Retrieval
Fanjie Kong, Shuai Yuan, Weituo Hao +1
We address the challenge of generating fair and unbiased image retrieval results given neutral textual queries (with no explicit gender or race connotations), while maintaining the…
Open World Classification with Adaptive Negative Samples
Ke Bai, Guoyin Wang, Jiwei Li +5
Open world classification is a task in natural language processing with key practical relevance and impact. Since the open or {\em unknown} category data only manifests in the infe…
Toward Fairness in Text Generation via Mutual Information Minimization based on Importance Sampling
Rui Wang, Pengyu Cheng, Ricardo Henao
Pretrained language models (PLMs), such as GPT2, have achieved remarkable empirical performance in text generation tasks. However, pretrained on large-scale natural language corpor…
Neural Insights for Digital Marketing Content Design
Fanjie Kong, Yuan Li, Houssam Nassif +3
In digital marketing, experimenting with new website content is one of the key levers to improve customer engagement. However, creating successful marketing content is a manual and…
Pushing the Efficiency Limit Using Structured Sparse Convolutions
Vinay Kumar Verma, Nikhil Mehta, Shijing Si +2
Weight pruning is among the most popular approaches for compressing deep convolutional neural networks. Recent work suggests that in a randomly initialized deep neural network, the…
Toward Sustainable Continual Learning: Detection and Knowledge Repurposing of Similar Tasks
Sijia Wang, Yoojin Choi, Junya Chen +2
Most existing works on continual learning (CL) focus on overcoming the catastrophic forgetting (CF) problem, with dynamic models and replay methods performing exceptionally well. H…
Flexible Triggering Kernels for Hawkes Process Modeling
Yamac Alican Isik, Connor Davis, Paidamoyo Chapfuwa +1
Recently proposed encoder-decoder structures for modeling Hawkes processes use transformer-inspired architectures, which encode the history of events via embeddings and self-attent…
Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations
Paidamoyo Chapfuwa, Sherri Rose, Lawrence Carin +2
End-to-end learning of dynamical systems with black-box models, such as neural ordinary differential equations (ODEs), provides a flexible framework for learning dynamics from data…
Gradient Importance Learning for Incomplete Observations
Qitong Gao, Dong Wang, Joshua D. Amason +6
Though recent works have developed methods that can generate estimates (or imputations) of the missing entries in a dataset to facilitate downstream analysis, most depend on assump…
Efficient Classification of Very Large Images with Tiny Objects
Fanjie Kong, Ricardo Henao
An increasing number of applications in computer vision, specially, in medical imaging and remote sensing, become challenging when the goal is to classify very large images with ti…
Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classification
Fanjie Kong, Xiao-yang Liu, Ricardo Henao
Tiny object classification problem exists in many machine learning applications like medical imaging or remote sensing, where the object of interest usually occupies a small region…
Wasserstein Contrastive Representation Distillation
Liqun Chen, Dong Wang, Zhe Gan +3
The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compa…
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models
Dong Wang, Yuewei Yang, Chenyang Tao +5
Deep neural networks excel at comprehending complex visual signals, delivering on par or even superior performance to that of human experts. However, ad-hoc visual explanations of…
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer
Zidi Xiu, Junya Chen, Ricardo Henao +3
Dealing with severe class imbalance poses a major challenge for real-world applications, especially when the accurate classification and generalization of minority classes is of pr…
Estimating Total Correlation with Mutual Information Estimators
Ke Bai, Pengyu Cheng, Weituo Hao +2
Total correlation (TC) is a fundamental concept in information theory that measures statistical dependency among multiple random variables. Recently, TC has shown noticeable effect…