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Found 14 papers, 7 papers with code
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning
Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset.
Uniform Inference for Nonlinear Endogenous Treatment Effects with High-Dimensional Covariates
In this paper, we propose new estimation and inference procedures for nonparametric treatment effect functions with endogeneity and potentially high-dimensional covariates.
Distributionally Robust Transfer Learning
Many existing transfer learning methods rely on leveraging information from source data that closely resembles the target data.
Distributionally Robust Machine Learning with Multi-source Data
Classical machine learning methods may lead to poor prediction performance when the target distribution differs from the source populations.
Datasets and Benchmarks for Offline Safe Reinforcement Learning
This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation of safe learning algorithms in both the training and deployment phases.
TSCI: two stage curvature identification for causal inference with invalid instruments
TSCI implements a two-stage algorithm.
Controllable Video Generation by Learning the Underlying Dynamical System with Neural ODE
Videos depict the change of complex dynamical systems over time in the form of discrete image sequences.
Constrained Decision Transformer for Offline Safe Reinforcement Learning
Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment.
On the Robustness of Safe Reinforcement Learning under Observational Perturbations
One interesting and counter-intuitive finding is that the maximum reward attack is strong, as it can both induce unsafe behaviors and make the attack stealthy by maintaining the reward.
A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates
The theoretical power based on the maximum norm is shown to be higher than that in the modified Cragg-Donald test (Koles\'{a}r, 2018), which is the only existing test allowing for large-dimensional covariates.
Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
The success of TSCI requires the instrumental variable's effect on treatment to differ from its violation form.
Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction
Risk modeling with EHR data is challenging due to a lack of direct observations on the disease outcome, and the high dimensionality of the candidate predictors.
Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies
Integrative analysis of data from multiple sources is critical to making generalizable discoveries.
Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding
Inferring causal relationships or related associations from observational data can be invalidated by the existence of hidden confounding.
Methodology Statistics Theory Statistics Theory
