9 papers
When and why randomised exploration works (in linear bandits)
Marc Abeille, David Janz, Ciara Pike-Burke
We provide an approach for the analysis of randomised exploration algorithms like Thompson sampling that does not rely on forced optimism or posterior inflation. With this, we demo…
On the necessity of adaptive regularisation:Optimal anytime online learning on -balls
Emmeran Johnson, David MartÃnez-Rubio, Ciara Pike-Burke +1
We study online convex optimization on -balls in for . While always sub-linear, the optimal regret exhibits a shift between the high-dimensional setti…
Stochastic Shortest Path with Sparse Adversarial Costs
Emmeran Johnson, Alberto Rumi, Ciara Pike-Burke +1
We study the adversarial Stochastic Shortest Path (SSP) problem with sparse costs under full-information feedback. In the known transition setting, existing bounds based on Online…
Locally Differentially Private Thresholding Bandits
Annalisa Barbara, Joseph Lazzaro, Ciara Pike-Burke
This work investigates the impact of ensuring local differential privacy in the thresholding bandit problem. We consider both the fixed budget and fixed confidence settings. We pro…
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…
Learning Fair And Effective Points-Based Rewards Programs
Chamsi Hssaine, Yichun Hu, Ciara Pike-Burke
Points-based rewards programs are a prevalent way to incentivize customer loyalty; in these programs, customers who make repeated purchases from a seller accumulate points, working…