6 citations · 8 across the 8 of their papers we have counts for
5 papers · 1 filter
On the Computational Tractability of Robust Bandits
Vanessa Kosoy, Vinayak Pathak
Learning when the environment does not belong to the learner's hypothesis class is typically handled using agnostic learning guarantees. However, for anything beyond supervised lea…
Ambiguous Online Learning
Vanessa Kosoy
We propose a new variant of online learning that we call "ambiguous online learning". In this setting, the learner is allowed to produce multiple predicted labels. Such an "ambiguo…
Regret Bounds for Robust Online Decision Making
Alexander Appel, Vanessa Kosoy
We propose a framework which generalizes "decision making with structured observations" by allowing robust (i.e. multivalued) models. In this framework, each model associates each…
Imprecise Multi-Armed Bandits
Vanessa Kosoy
We introduce a novel multi-armed bandit framework, where each arm is associated with a fixed unknown credal set over the space of outcomes (which can be richer than just the reward…
Delegative Reinforcement Learning: learning to avoid traps with a little help
Vanessa Kosoy
Most known regret bounds for reinforcement learning are either episodic or assume an environment without traps. We derive a regret bound without making either assumption, by allowi…