359,930 research outputs found
Online Algorithms with Uncertainty-Quantified Predictions
Online algorithms with predictions have become a trending topic in the field
of beyond worst-case analysis of algorithms. These algorithms incorporate
predictions about the future to obtain performance guarantees that are of high
quality when the predictions are good, while still maintaining bounded
worst-case guarantees when predictions are arbitrarily poor. In general, the
algorithm is assumed to be unaware of the prediction's quality. However, recent
developments in the machine learning literature have studied techniques for
providing uncertainty quantification on machine-learned predictions, which
describes how certain a model is about its quality. This paper examines the
question of how to optimally utilize uncertainty-quantified predictions in the
design of online algorithms. In particular, we consider predictions augmented
with uncertainty quantification describing the likelihood of the ground truth
falling in a certain range, designing online algorithms with these
probabilistic predictions for two classic online problems: ski rental and
online search. In each case, we demonstrate that non-trivial modifications to
algorithm design are needed to fully leverage the probabilistic predictions.
Moreover, we consider how to utilize more general forms of uncertainty
quantification, proposing a framework based on online learning that learns to
exploit uncertainty quantification to make optimal decisions in multi-instance
settings
Recommended from our members
The limits of human predictions of recidivism.
Dressel and Farid recently found that laypeople were as accurate as statistical algorithms in predicting whether a defendant would reoffend, casting doubt on the value of risk assessment tools in the criminal justice system. We report the results of a replication and extension of Dressel and Farid's experiment. Under conditions similar to the original study, we found nearly identical results, with humans and algorithms performing comparably. However, algorithms beat humans in the three other datasets we examined. The performance gap between humans and algorithms was particularly pronounced when, in a departure from the original study, participants were not provided with immediate feedback on the accuracy of their responses. Algorithms also outperformed humans when the information provided for predictions included an enriched (versus restricted) set of risk factors. These results suggest that algorithms can outperform human predictions of recidivism in ecologically valid settings
Adaptive scanning - a proposal how to scan theoretical predictions over a multi-dimensional parameter space efficiently
A method is presented to exploit adaptive integration algorithms using
importance sampling, like VEGAS, for the task of scanning theoretical
predictions depending on a multi-dimensional parameter space. Usually, a
parameter scan is performed with emphasis on certain features of a theoretical
prediction. Adaptive integration algorithms are well-suited to perform this
task very efficiently. Predictions which depend on parameter spaces with many
dimensions call for such an adaptive scanning algorithm.Comment: 8 pages, 4 figure
Online Algorithms for Weighted Paging with Predictions
In this paper, we initiate the study of the weighted paging problem with predictions. This continues the recent line of work in online algorithms with predictions, particularly that of Lykouris and Vassilvitski (ICML 2018) and Rohatgi (SODA 2020) on unweighted paging with predictions. We show that unlike unweighted paging, neither a fixed lookahead nor knowledge of the next request for every page is sufficient information for an algorithm to overcome existing lower bounds in weighted paging. However, a combination of the two, which we call the strong per request prediction (SPRP) model, suffices to give a 2-competitive algorithm. We also explore the question of gracefully degrading algorithms with increasing prediction error, and give both upper and lower bounds for a set of natural measures of prediction error
- …