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- W3033245173 abstract "We study sample complexity of optimizing hill-climbing friendly functions defined on a graph under noisy observations. We define a notion of convexity, and we show that a variant of best-arm identification can find a near-optimal solution after a small number of queries that is independent of the size of the graph. For functions that have local minima and are nearly convex, we show a sample complexity for the classical simulated annealing under noisy observations. We show effectiveness of the greedy algorithm with restarts and the simulated annealing on problems of graph-based nearest neighbor classification as well as a web document re-ranking application." @default.
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- W3033245173 date "2020-06-04" @default.
- W3033245173 modified "2023-09-25" @default.
- W3033245173 title "Sample Efficient Graph-Based Optimization with Noisy Observations" @default.
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- W3033245173 doi "https://doi.org/10.48550/arxiv.2006.02672" @default.
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