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Found 5 papers, 3 papers with code
Recursive Causal Discovery
Presence and identification of removable variables allow recursive approaches for causal discovery, a promising solution that helps to address the aforementioned challenges by reducing the problem size successively.
Contextual Combinatorial Multi-output GP Bandits with Group Constraints
In federated multi-armed bandit problems, maximizing global reward while satisfying minimum privacy requirements to protect clients is the main goal.
Contextual Combinatorial Bandits with Changing Action Sets via Gaussian Processes
We consider a contextual bandit problem with a combinatorial action set and time-varying base arm availability.
Contextual Combinatorial Volatile Multi-armed Bandit with Adaptive Discretization
We consider contextual combinatorial volatile multi-armed bandit (CCV-MAB), in which at each round, the learner observes a set of available base arms and their contexts, and then, selects a super arm that contains $K$ base arms in order to maximize its cumulative reward.
Pareto Active Learning with Gaussian Processes and Adaptive Discretization
We consider the problem of optimizing a vector-valued objective function $\boldsymbol{f}$ sampled from a Gaussian Process (GP) whose index set is a well-behaved, compact metric space $({\cal X}, d)$ of designs.
