research
∙
09/12/2023
Distributionally Robust Transfer Learning
Many existing transfer learning methods rely on leveraging information f...
share
research
∙
09/05/2023
Distributionally Robust Machine Learning with Multi-source Data
Classical machine learning methods may lead to poor prediction performan...
research
∙
06/15/2023
Datasets and Benchmarks for Offline Safe Reinforcement Learning
This paper presents a comprehensive benchmarking suite tailored to offli...
research
∙
04/02/2023
TSCI: two stage curvature identification for causal inference with invalid instruments
TSCI implements treatment effect estimation from observational data unde...
research
∙
03/09/2023
Controllable Video Generation by Learning the Underlying Dynamical System with Neural ODE
Videos depict the change of complex dynamical systems over time in the f...
research
∙
02/14/2023
Constrained Decision Transformer for Offline Safe Reinforcement Learning
Safe reinforcement learning (RL) trains a constraint satisfaction policy...
research
∙
02/09/2023
Surrogate-Assisted Federated Learning of high dimensional Electronic Health Record Data
Surrogate variables in electronic health records (EHR) play an important...
research
∙
01/25/2023
Statistical Inference and Large-scale Multiple Testing for High-dimensional Regression Models
This paper presents a selective survey of recent developments in statist...
research
∙
01/11/2023
RobustIV and controlfunctionIV: Causal Inference for Linear and Nonlinear Models with Invalid Instrumental Variables
We present R software packages RobustIV and controlfunctionIV for causal...
research
∙
01/02/2023
Robust Inference for Federated Meta-Learning
Synthesizing information from multiple data sources is critical to ensur...
research
∙
05/29/2022
On the Robustness of Safe Reinforcement Learning under Observational Perturbations
Safe reinforcement learning (RL) trains a policy to maximize the task re...
research
∙
04/30/2022
Testing Overidentifying Restrictions with High-Dimensional Data and Heteroskedasticity
This paper proposes a new test of overidentifying restrictions (called t...
research
∙
03/24/2022
Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables
Instrumental variables regression is a popular causal inference method f...
research
∙
02/21/2022
Statistical Inference for Genetic Relatedness Based on High-Dimensional Logistic Regression
This paper studies the problem of statistical inference for genetic rela...
research
∙
09/07/2021
SIHR: An R Package for Statistical Inference in High-dimensional Linear and Logistic Regression Models
We introduce and illustrate through numerical examples the R package wh...
research
∙
05/04/2021
Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction
Risk modeling with EHR data is challenging due to a lack of direct obser...
research
∙
04/14/2021
Post-selection Problems for Causal Inference with Invalid Instruments: A Solution Using Searching and Sampling
Instrumental variable method is among the most commonly used causal infe...
research
∙
12/13/2020
Inference for the Case Probability in High-dimensional Logistic Regression
Labeling patients in electronic health records with respect to their sta...
research
∙
11/15/2020
Inference for High-dimensional Maximin Effects in Heterogeneous Regression Models Using a Sampling Approach
Heterogeneity is an important feature of modern data sets and a central ...
research
∙
10/19/2020
Causal Inference for Nonlinear Outcome Models with Possibly Invalid Instrumental Variables
Instrumental variable methods are widely used for inferring the causal e...
research
∙
04/08/2020
Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding and Measurement Errors
Inferring causal relationships or related associations from observationa...
research
∙
09/04/2019
Group Inference in High Dimensions with Applications to Hierarchical Testing
Group inference has been a long-standing question in statistics and the ...
research
∙
07/30/2019
Local Inference in Additive Models with Decorrelated Local Linear Estimator
Additive models, as a natural generalization of linear regression, have ...
research
∙
04/29/2019
Extreme Nonlinear Correlation for Multiple Random Variables and Stochastic Processes with Applications to Additive Models
The maximum correlation of functions of a pair of random variables is an...
research
∙
04/29/2019
Individualized Treatment Selection: An Optimal Hypothesis Testing Approach In High-dimensional Models
The ability to predict individualized treatment effects (ITEs) based on ...
research
∙
06/16/2018
