no code implementations • 3 Feb 2023 • Santiago Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
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.
no code implementations • 27 Jun 2022 • Santiago Balseiro, Christian Kroer, Rachitesh Kumar
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.
no code implementations • 18 Feb 2022 • Santiago Balseiro, Christian Kroer, Rachitesh Kumar
Moreover, we provide an online algorithm that always achieves performance on this Pareto frontier.
no code implementations • 5 Mar 2021 • Santiago Balseiro, Omar Besbes, Francisco Castro
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.
no code implementations • 18 Nov 2020 • Santiago Balseiro, Haihao Lu, Vahab Mirrokni
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.
no code implementations • 1 Jul 2020 • Santiago Balseiro, Haihao Lu, Vahab Mirrokni
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.
no code implementations • ICML 2020 • Haihao Lu, Santiago Balseiro, Vahab Mirrokni
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
no code implementations • NeurIPS 2019 • Santiago Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, Jon Schneider
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 $c'\in \mathcal{O}_i(c)$.
no code implementations • NeurIPS 2017 • Santiago Balseiro, Max Lin, Vahab Mirrokni, Renato Leme, Iiis Song Zuo
In this paper, we characterize the optimal revenue sharing scheme that satisfies both constraints in expectation.