4 papers
Randomized Exploration for Linear Bandits via Absolute Perturbations
Toshinori Kitamura, Shuai Liu, Csaba Szepesvári
In stochastic linear bandits, the canonical Upper Confidence Bound (UCB) algorithm admits a simple frequentist regret analysis but can be computationally demanding, while Thompson…
Exploration via linearly perturbed loss minimisation
David Janz, Shuai Liu, Alex Ayoub +1
We introduce exploration via linear loss perturbations (EVILL), a randomised exploration method for structured stochastic bandit problems that works by solving for the minimiser of…
Learning What to Recommend: Minimax Optimal Simple Regret in Logistic Bandits
Shuai Liu, Alireza Bakhtiari, Alex Ayoub +2
We study stochastic logistic bandits with -dimensional action features under the simple-regret objective, where a learner uses rounds of exploration to output a single final…
Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits
Shuai Liu, Alex Ayoub, Flore Sentenac +2
We prove that single-parameter natural exponential families with subexponential tails are self-concordant with polynomial-sized parameters. For subgaussian natural exponential fami…