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Found 4 papers, 2 papers with code
Causal Bandits without Graph Learning
We study the causal bandit problem when the causal graph is unknown and develop an efficient algorithm for finding the parent node of the reward node using atomic interventions.
Trace norm regularization for multi-task learning with scarce data
Multi-task learning leverages structural similarities between multiple tasks to learn despite very few samples.
A Distribution-Dependent Analysis of Meta-Learning
A key problem in the theory of meta-learning is to understand how the task distributions influence transfer risk, the expected error of a meta-learner on a new task drawn from the unknown task distribution.
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