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Approximation Guarantees for Data-Driven Algorithm Design
Simons Institute for the Theory of Computing
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Approximation Algorithms for Optimization under Uncertainty
Simons Institute for the Theory of Computing
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Machine Learning For Algorithm Design
Simons Institute for the Theory of Computing
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Theoretical Foundations of Data-Driven Algorithm Design
Simons Institute for the Theory of Computing
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Madeleine Udell: "Low Rank Tucker Approximation of a Tensor from Streaming Data"
Institute for Pure & Applied Mathematics (IPAM)
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Ellen Vitercik: "How much data is sufficient to learn high-performing algorithms?"
Institute for Pure & Applied Mathematics (IPAM)
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On Approximation Guarantees for Greedy Low Rank Optimization
Simons Institute for the Theory of Computing
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