32 citations · 46 across the 18 of their papers we have counts for
18 papers
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework
Yangyi Chen, Hongcheng Gao, Ganqu Cui +10
Textual adversarial attacks can discover models' weaknesses by adding semantic-preserved but misleading perturbations to the inputs. The long-lasting adversarial attack-and-defense…
Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning
Ruixiang Tang, Dehan Kong, Longtao Huang +1
Large language models (LLMs) have recently shown great potential for in-context learning, where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts). D…
The synthetic instrument: From sparse association to sparse causation
Dingke Tang, Dehan Kong, Linbo Wang
In many observational studies, researchers are often interested in studying the effects of multiple exposures on a single outcome. Standard approaches for high-dimensional data suc…
Learning Brain Connectivity in Social Cognition with Dynamic Network Regression
Maoyu Zhang, Biao Cai, Wenlin Dai +3
Dynamic networks have been increasingly used to characterize brain connectivity that varies during resting and task states. In such characterizations, a connectivity network is typ…
Towards R-learner with Continuous Treatments
Yichi Zhang, Dehan Kong, Shu Yang
The R-learner is widely used in causal inference due to its flexibility and efficiency in estimating the conditional average treatment effect. However, extending the R-learner fram…
Fighting Noise with Noise: Causal Inference with Many Candidate Instruments
Xinyi Zhang, Linbo Wang, Stanislav Volgushev +1
Instrumental variable methods provide useful tools for inferring causal effects in the presence of unmeasured confounding. To apply these methods with large-scale data sets, a majo…
Supervised Principal Component Regression for Functional Responses with High Dimensional Predictors
Xinyi Zhang, Qiang Sun, Dehan Kong
We propose a supervised principal component regression method for relating functional responses with high dimensional predictors. Unlike the conventional principal component analys…
Causal Inference on Distribution Functions
Zhenhua Lin, Dehan Kong, Linbo Wang
Understanding causal relationships is one of the most important goals of modern science. So far, the causal inference literature has focused almost exclusively on outcomes coming f…
The Promises of Parallel Outcomes
Ying Zhou, Dingke Tang, Dehan Kong +1
A key challenge in causal inference from observational studies is the identification and estimation of causal effects in the presence of unmeasured confounding. In this paper, we i…
Multivariate functional responses low rank regression with an application to brain imaging data
Xiucai Ding, Dengdeng Yu, Zhengwu Zhang +1
We propose a multivariate functional responses low rank regression model with possible high dimensional functional responses and scalar covariates. By expanding the slope functions…
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images
Ming Y. Lu, Dehan Kong, Jana Lipkova +5
Deep Learning-based computational pathology algorithms have demonstrated profound ability to excel in a wide array of tasks that range from characterization of well known morpholog…
Ultra-high Dimensional Variable Selection for Doubly Robust Causal Inference
Dingke Tang, Dehan Kong, Wenliang Pan +1
Causal inference has been increasingly reliant on observational studies with rich covariate information. To build tractable causal procedures, such as the doubly robust estimators,…
Covariance Estimation for Matrix-valued Data
Yichi Zhang, Weining Shen, Dehan Kong
Covariance estimation for matrix-valued data has received an increasing interest in applications. Unlike previous works that rely heavily on matrix normal distribution assumption a…
Nonparametric principal subspace regression
Mark Koudstaal, Dengdeng Yu, Dehan Kong +1
In scientific applications, multivariate observations often come in tandem with temporal or spatial covariates, with which the underlying signals vary smoothly. The standard approa…
Modeling Symmetric Positive Definite Matrices with An Application to Functional Brain Connectivity
Zhenhua Lin, Dehan Kong, Qiang Sun
In neuroscience, functional brain connectivity describes the connectivity between brain regions that share functional properties. Neuroscientists often characterize it by a time se…
Identifiability of causal effects with multiple causes and a binary outcome
Dehan Kong, Shu Yang, Linbo Wang
Unobserved confounding presents a major threat to causal inference from observational studies. Recently, several authors suggest that this problem may be overcome in a shared confo…
Nonparametric Matrix Response Regression with Application to Brain Imaging Data Analysis
Wei Hu, Tianyu Pan, Dehan Kong +1
With the rapid growth of neuroimaging technologies, a great effort has been dedicated recently to investigate the dynamic changes in brain activity. Examples include time course ca…
Matrix Linear Discriminant Analysis
Wei Hu, Weining Shen, Hua Zhou +1
We propose a novel linear discriminant analysis approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies. Motivated by the e…