56 citations · 65 across the 18 of their papers we have counts for
7 papers · 1 filter
Fixed-Confidence Multiple Change Point Identification under Bandit Feedback
Joseph Lazzaro, Ciara Pike-Burke
Piecewise constant functions describe a variety of real-world phenomena in domains ranging from chemistry to manufacturing. In practice, it is often required to confidently identif…
Fixed-Budget Change Point Identification in Piecewise Constant Bandits
Joseph Lazzaro, Ciara Pike-Burke
We study the piecewise constant bandit problem where the expected reward is a piecewise constant function with one change point (discontinuity) across the action space and…
Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts
Dirk van der Hoeven, Ciara Pike-Burke, Hao Qiu +1
We investigate online classification with paid stochastic experts. Here, before making their prediction, each expert must be paid. The amount that we pay each expert directly influ…
Delayed Feedback in Kernel Bandits
Sattar Vakili, Danyal Ahmed, Alberto Bernacchia +1
Black box optimisation of an unknown function from expensive and noisy evaluations is a ubiquitous problem in machine learning, academic research and industrial production. An abst…
Bandit problems with fidelity rewards
Gábor Lugosi, Ciara Pike-Burke, Pierre-André Savalle
The fidelity bandits problem is a variant of the -armed bandit problem in which the reward of each arm is augmented by a fidelity reward that provides the player with an additio…
Recovering Bandits
Ciara Pike-Burke, Steffen Grünewälder
We study the recovering bandits problem, a variant of the stochastic multi-armed bandit problem where the expected reward of each arm varies according to some unknown function of t…
Bandits with Delayed, Aggregated Anonymous Feedback
Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvari +1
We study a variant of the stochastic -armed bandit problem, which we call "bandits with delayed, aggregated anonymous feedback". In this problem, when the player pulls an arm, a…