"Tuning"的相关文档

Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
GuaranteesforTuningtheStepSizeusingaLearning-to-LearnApproachXiangWang1ShuaiYuan1ChenweiWu1RongGe1Abstractrinetal.(2015)consideredtheideaofTuningtheseparame-tersbyoptimization—thatis,considerameta...
for Using Guarantees the Tuning
2023-11-16 18:46:52717372.01 KB23
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The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers
TheCost-freeNatureofOptimallyTuningTikhonovRegularizersandOtherOrderedSmoothersPierreC.Bellec1DanaYang2AbstractisdeﬁnedasthesolutionofthequadraticprogramWeconsidertheproblemofselectingthebestes-w...
of the Tuning optimally Cost-free
2023-11-14 21:46:435871.17 MB20
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On hyperparameter Tuning in general clustering problemsm
OnhyperparameterTuningingeneralclusteringproblemsXinjieFan1YuguangYue1PurnamritaSarkar1Y.X.RachelWang2Abstractsiblehyperparametervaluesandoftenhastorelyongoodheuristicsanddomainknowledgefromtheuser...
on in Clustering General Hyperparameter
2023-11-14 21:45:2917971.02 MB16
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Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data
OptimalTuningforDivide-and-conquerKernelRidgeRegressionwithMassiveDataGanggangXu1ZuofengShang2GuangCheng3AbstractproventobeeffectiveinLinearmodels(Chen&Xie,2014;Luetal.,2016),Partiallylinearmodels(...
for Kernel Regression Optimal Ridge
2023-11-13 12:00:221862324.76 KB6
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Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
ApproximateLeave-One-OutforFastParameterTuninginHighDimensionsShuaiwenWang1WendaZhou1HaihaoLu2ArianMaleki1VahabMirrokni3Abstractschemes,especiallyinhigh-dimensionalsettings,studiesthefollowingoptim...
for Approximate in Fast Leave-One-Out
2023-11-13 11:59:0412944.08 MB24
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Guarantees for Tuning the Step Size using a Learning-to-Learn Approach

The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers

On hyperparameter Tuning in general clustering problemsm

Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data

Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions