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Found 3 papers, 1 papers with code
TAME: Task Agnostic Continual Learning using Multiple Experts
Our algorithm, which we call TAME (Task-Agnostic continual learning using Multiple Experts), automatically detects the shift in data distributions and switches between task expert networks in an online manner.
Efficient Contextual Bandits with Continuous Actions
We create a computationally tractable algorithm for contextual bandits with continuous actions having unknown structure.
LdSM: Logarithm-depth Streaming Multi-label Decision Trees
In this paper we develop the LdSM algorithm for the construction and training of multi-label decision trees, where in every node of the tree we optimize a novel objective function that favors balanced splits, maintains high class purity of children nodes, and allows sending examples to multiple directions but with a penalty that prevents tree over-growth.
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