Introduces OAG model and DTB compiler for learning-augmented online algorithms, achieving strong consistency-robustness trade-offs on bipartite matching, caching, and metrical task systems.
Karp, Umesh V
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A local selection rule based on a fractional solution of the expected instance preserves the expected maximum matching size under sufficient spread and yields near-optimal global matchings with small local budgets on ride-hailing data.
citing papers explorer
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Online Algorithms with Unreliable Guidance
Introduces OAG model and DTB compiler for learning-augmented online algorithms, achieving strong consistency-robustness trade-offs on bipartite matching, caching, and metrical task systems.
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Stochastic Matching via Local Sparsification
A local selection rule based on a fractional solution of the expected instance preserves the expected maximum matching size under sufficient spread and yields near-optimal global matchings with small local budgets on ride-hailing data.