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Active Learning for Non-Parametric Choice Models
To overcome the identifiability problem, we introduce a directed acyclic graph (DAG) representation of the choice model.
Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds
We revisit the problem of online learning with sleeping experts/bandits: in each time step, only a subset of the actions are available for the algorithm to choose from (and learn about).
The Complexity of Interactively Learning a Stable Matching by Trial and Error
In a stable matching setting, we consider a query model that allows for an interactive learning algorithm to make precisely one type of query: proposing a matching, the response to which is either that the proposed matching is stable, or a blocking pair (chosen adversarially) indicating that this matching is unstable.
A General Framework for Robust Interactive Learning
Our general framework is based on a graph representation of the models and user feedback.
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