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cs.LG2026
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…
cs.LG2026
Variance-sensitive Thompson sampling for generalised linear bandits, revisited
Tom Perneczky, Marc Abeille, David Janz
We prove a variance-sensitive regret bound for Thompson sampling in stochastic generalised linear bandits. The argument assumes a warm-up, after which the regret is controlled thro…
cs.LG2025
Multi-Armed Bandits with Minimum Aggregated Revenue Constraints
Ahmed Ben Yahmed, Hafedh El Ferchichi, Marc Abeille +1
We examine a multi-armed bandit problem with contextual information, where the objective is to ensure that each arm receives a minimum aggregated reward across contexts while simul…