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Found 9 papers, 0 papers with code
Robust Budget Pacing with a Single Sample
Given the inherent non-stationarity in an advertiser's value and also competing advertisers' values over time, a commonly used approach is to learn a target expenditure plan that specifies a target spend as a function of time, and then run a controller that tracks this plan.
Online Resource Allocation under Horizon Uncertainty
We go on to give a fast algorithm for computing a schedule of target consumption rates that leads to near-optimal performance in the unknown-horizon setting.
Single-Leg Revenue Management with Advice
Moreover, we provide an online algorithm that always achieves performance on this Pareto frontier.
Mechanism Design under Approximate Incentive Compatibility
We establish that the gains that can be garnered depend on the local curvature of the seller's revenue function around the optimal posted price when the buyer is a perfect optimizer.
The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems
In this paper, we consider a data-driven setting in which the reward and resource consumption of each request are generated using an input model that is unknown to the decision maker.
Regularized Online Allocation Problems: Fairness and Beyond
In this paper, we introduce the \emph{regularized online allocation problem}, a variant that includes a non-linear regularizer acting on the total resource consumption.
Dual Mirror Descent for Online Allocation Problems
The revenue function and resource consumption of each request are drawn independently and at random from a probability distribution that is unknown to the decision maker.
Optimization and Control
Contextual Bandits with Cross-learning
We consider the variant of this problem where in addition to receiving the reward $r_{i, t}(c)$, the learner also learns the values of $r_{i, t}(c')$ for some other contexts $c'$ in set $\mathcal{O}_i(c)$; i. e., the rewards that would have been achieved by performing that action under different contexts $c'\in \mathcal{O}_i(c)$.
Dynamic Revenue Sharing
In this paper, we characterize the optimal revenue sharing scheme that satisfies both constraints in expectation.
