65 citations · 74 across the 7 of their papers we have counts for
8 papers
Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes
Dhanajit Brahma, Ruolin Meng, Lawrence Carin +1
Estimating causal effects on right-censored time-to-event outcomes is challenging under unmeasured confounding. Proximal causal inference addresses unmeasured confounding through p…
Leveraging Language Models for Interpretable Analysis of Narratives in a Large Corpus
Eric A. Bai, Minling Zhou, Ricardo Henao +2
Narratives drive human behavior and lay at the core of geopolitics, but have eluded quantification that would permit measurement of their overlap and evolution. We present an inter…
Coupling Generative Modeling and an Autoencoder with the Causal Bridge
Ruolin Meng, Ming-Yu Chung, Dhanajit Brahma +2
We consider inferring the causal effect of a treatment (intervention) on an outcome of interest in situations where there is potentially an unobserved confounder influencing both t…
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…
Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning
Vinay K Verma, Nikhil Mehta, Kevin J Liang +2
Zero-shot learning (ZSL) is a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges remain. Recently, metho…
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…
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…
Deconvolutional Paragraph Representation Learning
Yizhe Zhang, Dinghan Shen, Guoyin Wang +3
Learning latent representations from long text sequences is an important first step in many natural language processing applications. Recurrent Neural Networks (RNNs) have become a…