Learning Graphical Models
Linear Uncertainty Quantification of Graphical Model Inference
Graphical models, capable of representing data with complex dependencies, are widely used across domains.
Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints
Real-world reinforcement learning (RL) applications often come with possibly infinite state and action space, and in such a situation classical RL algorithms developed in the tabular setting are not applicable anymore. A popular approach to overcoming this issue is by applying function approximation techniques to the underlying structures of the Markov decision processes (MDPs).
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets
Hierarchical Bayesian methods enable information sharing across regression problems on multiple groups of data.
Bayesian Optimistic Optimization: Optimistic
In this paper, we consider the RL in Markov decision processes (MDPs), where the agent observes the state of the environment at each timestep and makes decisions accordingly.
A Additive feature attribution methods unify existing explainers for GNNs In this section, we analyze the vanilla gradient-based explainers and GNNExplainer [ 24
GNN and assign important scores on explained features. Here, we consider the simplest gradient-based explanation method in which the score of each feature is associated with the gradient of the GNN's loss function with respect to that feature. The proof that this explanation method falls into the class of additive feature attribution methods is quite straight-forward. S is a good explanation for the target prediction. This experiment setup is the same as that in experiment of Figure 1.
