4 papers · 1 filter
Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information
Maria-Florina Balcan, Martino Bernasconi, Matteo Castiglioni +3
We study the problem of online learning in Stackelberg games with side information between a leader and a sequence of followers. In every round the leader observes contextual infor…
Online Learning with Sublinear Best-Action Queries
Matteo Russo, Andrea Celli, Riccardo Colini Baldeschi +5
In online learning, a decision maker repeatedly selects one of a set of actions, with the goal of minimizing the overall loss incurred. Following the recent line of research on alg…
Beyond Primal-Dual Methods in Bandits with Stochastic and Adversarial Constraints
Martino Bernasconi, Matteo Castiglioni, Andrea Celli +1
We address a generalization of the bandit with knapsacks problem, where a learner aims to maximize rewards while satisfying an arbitrary set of long-term constraints. Our goal is t…
No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization
Martino Bernasconi, Matteo Castiglioni, Andrea Celli
In the bandits with knapsacks framework (BwK) the learner has resource-consumption (packing) constraints. We focus on the generalization of BwK in which the learner has a set o…