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Found 4 papers, 3 papers with code
Sorting with Predictions
We explore the fundamental problem of sorting through the lens of learning-augmented algorithms, where algorithms can leverage possibly erroneous predictions to improve their efficiency.
Mixing predictions for online metric algorithms
Since the performance of each predictor may vary over time, it is desirable to use not the single best predictor as a benchmark, but rather a dynamic combination which follows different predictors at different times.
Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds
A key ingredient in our approach is a new algorithm for the online ski rental problem in the learning augmented setting with tight dependence on the prediction error.
Online metric algorithms with untrusted predictions
Still, decision-making systems that are based on such predictors need not only to benefit from good predictions but also to achieve a decent performance when the predictions are inadequate.
Data Structures and Algorithms
