Machine-learned predictors, although achieving very good results for inputs resembling training data, cannot possibly provide perfect predictions in all situations. Still, decision …
We consider the question of speeding up classic graph algorithms with machine-learned predictions. In this model, algorithms are furnished with extra advice learned from past or …
A Wei, F Zhang - Advances in Neural Information …, 2020 - proceedings.neurips.cc
We study the problem of improving the performance of online algorithms by incorporating machine-learned predictions. The goal is to design algorithms that are both consistent and …
The secretary problem is probably the purest model of decision making under uncertainty. In this paper we ask which advice can we give the algorithm to improve its success probability …
There has been recent interest in using machine-learned predictions to improve the worst- case guarantees of online algorithms. In this paper we continue this line of work by studying …
K Anand, R Ge, A Kumar… - … Conference on Machine …, 2022 - proceedings.mlr.press
This paper studies online algorithms augmented with multiple machine-learned predictions. We give a generic algorithmic framework for online covering problems with multiple …
In the single-machine non-clairvoyant scheduling problem, the goal is to minimize the total completion time of jobs whose processing times are unknown a priori. We revisit this well …
In this work we introduce an alternative model for the design and analysis of strategyproof mechanisms that is motivated by the recent surge of work in" learning-augmented …
We propose a new model for augmenting algorithms with predictions by requiring that they are formally learnable and instance robust. Learnability ensures that predictions can be …