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Found 5 papers, 1 papers with code
Contextual Pandora's Box
Pandora's Box is a fundamental stochastic optimization problem, where the decision-maker must find a good alternative while minimizing the search cost of exploring the value of each alternative.
Online Learning for Min Sum Set Cover and Pandora's Box
In Pandora's Box, we are presented with $n$ boxes, each containing an unknown value and the goal is to open the boxes in some order to minimize the sum of the search cost and the smallest value found.
Approximating Pandora's Box with Correlations
For distributions of support $m$, UDT admits a $\log m$ approximation, and while a constant factor approximation in polynomial time is a long-standing open problem, constant factor approximations are achievable in subexponential time (arXiv:1906. 11385).
Learning Augmented Online Facility Location
We prove that the competitive ratio decreases smoothly from sublogarithmic in the number of demands to constant, as the error, i. e., the total distance of the predicted locations to the optimal facility locations, decreases towards zero.
Black-box Methods for Restoring Monotonicity
In this work we develop algorithms that are able to restore monotonicity in the parameters of interest.
