Statistical Learning
Covariate Shift Corrected Conditional Randomization Test
To address this issue, we propose a novel Covariate Shift Corrected Pearson Chi-squared Conditional Randomization (csPCR) test.
Particle-based Variational Inference with Generalized Wasserstein Gradient Flow
We show that GWG exhibits strong convergence guarantees.
Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity
How neuronal circuits achieve credit assignment remains a central unsolved question in systems neuroscience.
Tighter Convergence Bounds for Shuffled SGD via Primal-Dual Perspective
Appendix E), the variance across multiple runs is negligible, hence the ribbons are not observable.
Externally Valid Policy Evaluation from Randomized Trials Using Additional Observational Data
Randomized trials are widely considered as the gold standard for evaluating the effects of decision policies.
An Efficient Doubly-Robust Test for the Kernel Treatment Effect
Such assumptions allow for identification of causal target parameters. For instance, one may be interested in the average variance-weighted treatment effects (Robins et al., 2008; Li et al., 2011),
On the Convergence of CART under Sufficient Impurity Decrease Condition
The decision tree is a flexible machine learning model that finds its success in numerous applications. It is usually fitted in a recursively greedy manner using CART.