8 citations · 13 across the 7 of their papers we have counts for
6 papers · 1 filter
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification
James A. Grant, David S. Leslie
We consider a variant of online binary classification where a learner sequentially assigns labels ( or ) to items with unknown true class. If, but only if, the learner choose…
Learning to Rank under Multinomial Logit Choice
James A. Grant, David S. Leslie
Learning the optimal ordering of content is an important challenge in website design. The learning to rank (LTR) framework models this problem as a sequential problem of selecting…
Filtered Poisson Process Bandit on a Continuum
James A. Grant, Roberto Szechtman
We consider a version of the continuum armed bandit where an action induces a filtered realisation of a non-homogeneous Poisson process. Point data in the filtered sample are then…
On Thompson Sampling for Smoother-than-Lipschitz Bandits
James A. Grant, David S. Leslie
Thompson Sampling is a well established approach to bandit and reinforcement learning problems. However its use in continuum armed bandit problems has received relatively little at…
Adaptive Policies for Perimeter Surveillance Problems
James A. Grant, David S. Leslie, Kevin Glazebrook +2
Maximising the detection of intrusions is a fundamental and often critical aim of perimeter surveillance. Commonly, this requires a decision-maker to optimally allocate multiple se…
Combinatorial Multi-Armed Bandits with Filtered Feedback
James A. Grant, David S. Leslie, Kevin Glazebrook +1
Motivated by problems in search and detection we present a solution to a Combinatorial Multi-Armed Bandit (CMAB) problem with both heavy-tailed reward distributions and a new class…