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Found 16 papers, 6 papers with code
Conformal Diffusion Models for Individual Treatment Effect Estimation and Inference
Estimating treatment effects from observational data is of central interest across numerous application domains.
Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance
Imbalanced data and spurious correlations are common challenges in machine learning and data science.
Testing for the Markov Property in Time Series via Deep Conditional Generative Learning
In this article, we propose a nonparametric test for the Markov property in high-dimensional time series via deep conditional generative learning.
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach
In this article, we propose a novel pessimism-based Bayesian learning method for optimal dynamic treatment regimes in the offline setting.
Post-Regularization Confidence Bands for Ordinary Differential Equations
In this article, we construct post-regularization confidence band for individual regulatory function in ODE with unknown functionals and noisy data observations.
Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning
In this article, we propose a new hypothesis testing method for directed acyclic graph (DAG).
Nonparametric Trace Regression in High Dimensions via Sign Series Representation
Trace regression is a widely used method to model effects of matrix predictors and has shown great success in matrix learning.
Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis
Our proposal enjoys, to a good extent, both model interpretability and model flexibility.
Image Response Regression via Deep Neural Networks
Delineating the associations between images and a vector of covariates is of central interest in medical imaging studies.
Double Generative Adversarial Networks for Conditional Independence Testing
In this article, we study the problem of high-dimensional conditional independence testing, a key building block in statistics and machine learning.
Partially Observed Dynamic Tensor Response Regression
In this article, we develop a regression model with partially observed dynamic tensor as the response and external covariates as the predictor.
Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks
With the widespread success of deep neural networks in science and technology, it is becoming increasingly important to quantify the uncertainty of the predictions produced by deep learning.
Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality
We consider the problem of decomposing a higher-order tensor with binary entries.
Dynamic Tensor Clustering
Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor.
STORE: Sparse Tensor Response Regression and Neuroimaging Analysis
Motivated by applications in neuroimaging analysis, we propose a new regression model, Sparse TensOr REsponse regression (STORE), with a tensor response and a vector predictor.
