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Found 5 papers, 1 papers with code
Directional Optimism for Safe Linear Bandits
Lastly, we introduce a generalization of the safe linear bandit setting where the constraints are convex and adapt our algorithms and analyses to this setting by leveraging a novel convex-analysis based approach.
Safe Pricing Mechanisms for Distributed Resource Allocation with Bandit Feedback
In societal-scale infrastructures, such as electric grids or transportation networks, pricing mechanisms are often used as a way to shape users' demand in order to lower operating costs and improve reliability.
The Impact of the Geometric Properties of the Constraint Set in Safe Optimization with Bandit Feedback
Simulation results for this algorithm support the sublinear regret bound and provide empirical evidence that the sharpness of the constraint set impacts the performance of the algorithm.
Robust Distributed Optimization With Randomly Corrupted Gradients
In this paper, we propose a first-order distributed optimization algorithm that is provably robust to Byzantine failures-arbitrary and potentially adversarial behavior, where all the participating agents are prone to failure.
Feature and Parameter Selection in Stochastic Linear Bandits
In the second setting, the reward parameter of the LB problem is arbitrarily selected from $M$ models represented as (possibly) overlapping balls in $\mathbb R^d$.
