Learning Graphical Models
Sample Efficient Bayesian Learning of Causal Graphs from Interventions
In general, interventional data, i.e., data collected after a perturbation in
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games
In this paper, we formalize a novel reinforcement learning model which explicitly represents the information structure.
Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound
Current P AC-Bayes generalisation bounds are restricted to scalar metrics of performance, such as the loss or error rate. However, one ideally wants more information-rich certificates that control the entire distribution of possible outcomes, such as the distribution of the test loss in regression, or the probabilities of different mis-classifications.
Ordering-Based Causal Discovery for Linear and Nonlinear Relations
Identifying causal relations from purely observational data typically requires additional assumptions on relations and/or noise.
