Learning to optimize: A primer and a benchmark
Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …
develop optimization methods, aiming at reducing the laborious iterations of hand …
Competitive caching with machine learned advice
T Lykouris, S Vassilvitskii - Journal of the ACM (JACM), 2021 - dl.acm.org
Traditional online algorithms encapsulate decision making under uncertainty, and give ways
to hedge against all possible future events, while guaranteeing a nearly optimal solution, as …
to hedge against all possible future events, while guaranteeing a nearly optimal solution, as …
Algorithms with predictions
M Mitzenmacher, S Vassilvitskii - Communications of the ACM, 2022 - dl.acm.org
Algorithms with predictions Page 1 JULY 2022 | VOL. 65 | NO. 7 | COMMUNICATIONS OF
THE ACM 33 viewpoints IMA GE B Y ANDRIJ BOR YS A SSOCIA TE S, USING SHUTTERS T …
THE ACM 33 viewpoints IMA GE B Y ANDRIJ BOR YS A SSOCIA TE S, USING SHUTTERS T …
Online scheduling via learned weights
Online algorithms are a hallmark of worst case optimization under uncertainty. On the other
hand, in practice, the input is often far from worst case, and has some predictable …
hand, in practice, the input is often far from worst case, and has some predictable …
The primal-dual method for learning augmented algorithms
E Bamas, A Maggiori… - Advances in Neural …, 2020 - proceedings.neurips.cc
The extension of classical online algorithms when provided with predictions is a new and
active research area. In this paper, we extend the primal-dual method for online algorithms …
active research area. In this paper, we extend the primal-dual method for online algorithms …
Near-optimal bounds for online caching with machine learned advice
D Rohatgi - Proceedings of the Fourteenth Annual ACM-SIAM …, 2020 - SIAM
In the model of online caching with machine learned advice, introduced by Lykouris and
Vassilvitskii, the goal is to solve the caching problem with an online algorithm that has …
Vassilvitskii, the goal is to solve the caching problem with an online algorithm that has …
Updatable learned index with precise positions
Index plays an essential role in modern database engines to accelerate the query
processing. The new paradigm of" learned index" has significantly changed the way of …
processing. The new paradigm of" learned index" has significantly changed the way of …
Optimal robustness-consistency trade-offs for learning-augmented online algorithms
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 …
machine-learned predictions. The goal is to design algorithms that are both consistent and …
Online algorithms with multiple predictions
This paper studies online algorithms augmented with multiple machine-learned predictions.
We give a generic algorithmic framework for online covering problems with multiple …
We give a generic algorithmic framework for online covering problems with multiple …
Online graph algorithms with predictions
Online algorithms with predictions is a popular and elegant framework for bypassing
pessimistic lower bounds in competitive analysis. In this model, online algorithms are …
pessimistic lower bounds in competitive analysis. In this model, online algorithms are …