1 code implementation • NeurIPS 2023 • Xingjian Bai, Christian Coester
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.
no code implementations • 4 Apr 2023 • Antonios Antoniadis, Christian Coester, Marek Eliáš, Adam Polak, Bertrand Simon
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.
1 code implementation • NeurIPS 2021 • Antonios Antoniadis, Christian Coester, Marek Eliáš, Adam Polak, Bertrand Simon
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.
1 code implementation • ICML 2020 • Antonios Antoniadis, Christian Coester, Marek Elias, Adam Polak, Bertrand Simon
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