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STOC'22 Workshop on Algorithms with Predictions
This workshop aims to cover recent developments in the area of “algorithms with predictions” (aka learning-augmented algorithms or data driven algorithms). These methods show how to parameterize algorithms so that they can adapt their behavior to the properties of the input distribution and consequently improve their performance, such as runtime, space, or quality of the solution. Generally speaking, a result in this area takes a problem with strong computational lower bounds (for instance on the competitive ratio), identifies a compact prediction that can be learned from real data, and gives a proof tying the performance of the algorithm to the quality of the underlying prediction. The field has blossomed with applications to classical streaming algorithms, online scheduling, clustering, filtering data structures, and many others. All of these methods guarantee improved performance when the predictions are good, and maintain nearly identical worst-case guarantees when they are not. The workshop will cover recent advances in different domains, and introduce newcomers to open problems in this area. |
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Invited Speakers:
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Schedule:TBD
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Previous Workshops:
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