Statistical Learning
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion
This prevailing paradigm heavily relies on the out-of-distribution (OOD) effect to influence the model's prediction.
Amortized Bayesian Experimental Design for Decision-Making
For example, in personalized medical diagnostics, a model is built based on historical data to facilitate an optimal treatment for a new patient (Bica et al., 2021).
Debiasing Conditional Stochastic Optimization Lie He
The sample-averaged gradient of the CSO objective is biased due to its nested structure, and therefore requires a high sample complexity for convergence. We introduce a general stochastic extrapolation technique that effectively reduces the bias.
Causal Inference in the Closed-Loop: Marginal Structural Models for Sequential Excursion Effects Alexander W. Levis
To fill this gap, we introduce a nonparametric causal inference framework for analyzing "closed-loop" designs, which use
