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标签“for”的相关文档，共1143条

Accurate Inference for Adaptive Linear Models
AccurateInferenceforAdaptiveLinearModelsYashDeshpande1LesterMackey2VasilisSyrgkanis2MattTaddy23Abstractthesample(yi,xi)i≤nisnotcompletelyrandomizedbutratheradaptivelychosen.Byadaptive,wemeanthatth...
Adaptive for Inference Models Linear
2023-11-13 11:58:58733596.56 KB7
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Active Learning for Accurate Estimation of Linear Models
ActiveLearningforAccurateEstimationofLinearModelsCarlosRiquelme1MohammadGhavamzadeh2AlessandroLazaric3Abstractassumethattheobservationsarecorruptedbynoiselevelsthatareproblem-dependentandmustbelear...
Learning of Active for Estimation
2023-11-12 20:45:3717224.02 MB22
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Active Learning for Cost-Sensitive Classification
ActiveLearningforCost-SensitiveClassiﬁcationAkshayKrishnamurthy1AlekhAgarwal2Tzu-KuoHuang3HalDaume´III4JohnLangford2Abstractcostsofonlya(possiblyempty)subsetoflabelsoneachx.Sincemeasuringlabelcom...
Learning Active for Classification Cost-Sensitive
2023-11-12 20:45:36600815.21 KB4
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A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery
AUniﬁedVarianceReduction-BasedFrameworkforNonconvexLow-RankMatrixRecoveryLingxiaoWang1XiaoZhang1QuanquanGu1Abstractlaxationbasedoptimization(Srebroetal.,2004;Cande`s&Tao,2010;Rohdeetal.,2011;Recht...
for Low-Rank Variance Nonconvex Framework
2023-11-12 20:45:35585685.08 KB14
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A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization
ASimulatedAnnealingBasedInexactOracleforWassersteinLossMinimizationJianboYe1JamesZ.Wang1JiaLi2Abstractryl(x,·)iscomputedinO(m)time,mbeingthecomplex-ityofoutcomevariablesxory.Thispartofcalculationi...
for Based Wasserstein Oracle Simulated
2023-11-12 20:45:357942.29 MB26
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A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions
AUniﬁedMaximumLikelihoodApproachforEstimatingSymmetricPropertiesofDiscreteDistributionsJayadevAcharya1HirakenduDas2AlonOrlitsky3AnandaTheerthaSuresh4Abstractapplications.Symmetricdistributionprope...
for Approach Estimating Maximum Symmetric
2023-11-12 20:45:35533477.9 KB2
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A Semismooth Newton Method for Fast, Generic Convex Programming
ASemismoothNewtonMethodforFast,GenericConvexProgrammingAlnurAli1EricWong1J.ZicoKolter2Abstracti.e.,thespaceofm×mpositivesemideﬁnitematricesSm+,yieldsasemideﬁniteprogram;andtakingKasthesecond-Wei...
for Newton Fast Convex Method
2023-11-12 20:45:341041765.24 KB3
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A Laplacian Framework for Option Discovery in Reinforcement Learning
ALaplacianFrameworkforOptionDiscoveryinReinforcementLearningMarlosC.Machado1MarcG.Bellemare2MichaelBowling1Abstracttheoptimalpolicyforthatrewardfunction.Inthispaperweintroduceanalgorithmforoptiondi...
for Reinforcement Discovery in Laplacian
2023-11-12 20:45:3317832.5 MB28
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A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI
ADivergenceBoundforHybridsofMCMCandVariationalInferenceandanApplicationtoLangevinDynamicsandSGVIJustinDomke1Abstractthem.Computingp(z)thusrequiresafullpassoverthedataset.TheideaofStochasticGradient...
of for and MCMC Divergence
2023-11-12 20:45:3316024.47 MB21
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A Birth-Death Process for Feature Allocation
ABirth-DeathProcessforFeatureAllocationKonstantinaPalla1DavidKnowles2ZoubinGhahramani34Abstract2.RelatedWorkWeproposeaBayesiannonparametricprioroverWetargetproblemswherethedatadependsonacovariate,f...
for Feature Process Allocation Birth-Death
2023-11-12 20:45:327181.03 MB26
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Zonotope Hit-and-run for Efficient Sampling from Projection DPPs
ZonotopeHit-and-runforEfﬁcientSamplingfromProjectionDPPs1212GuillaumeGautierRe´miBardenetMichalValkoAbstractformofaﬁnitematrix,seeKulesza&Taskar(2012)foracomprehensivesurvey.ApplicationsofDPPsin...
for from Efficient Sampling Projection
2023-11-12 20:45:3211944.26 MB25
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World of Bits An Open-Domain Platform for Web-Based Agents
WorldofBits:AnOpen-DomainPlatformforWeb-BasedAgentsTianlin(Tim)Shi12AndrejKarpathy2Linxi(Jim)Fan1JonathanHernandez2PercyLiang1AbstractFigure1.AgentsintheWorldofBitsperceivethescreenpixels,theDOM(wi...
of for An World Bits
2023-11-12 20:45:315453.23 MB8
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When can Multi-Site Datasets be Pooled for Regression Hypothesis Tests, $ ell_2$-consistency and Neuroscience Applications
WhencanMulti-SiteDatasetsbePooledforRegression?HypothesisTests,2-consistencyandNeuroscienceApplicationsHaoHenryZhou1YilinZhang1VamsiK.Ithapu1SterlingC.Johnson12GraceWahba1VikasSingh1Abstractofscien...
for Can Be When Multi-Site
2023-11-12 20:45:301334375.9 KB14
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Variational Inference for Sparse and Undirected Models
VariationalInferenceforSparseandUndirectedModelsJohnIngraham1DeboraMarks1AbstractL0norm,L1regularizationcannotmodelthehypothesisofsparsityaswellassomeBayesianalternatives(Tipping,Undirectedgraphica...
for Inference Sparse Models Variational
2023-11-12 20:45:3011364.05 MB22
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Variational Policy for Guiding Point Processes
VariationalPolicyforGuidingPointProcessesYichenWang1GradyWilliams2EvangelosTheodorou2LeSong1AbstractOurworkTemporalpointprocesseshavebeenwidelyap-Findoptimalmeasure6∗6∗inclosedformVariationalInfe...
for Variational Policy Processes Point
2023-11-12 20:45:296191.02 MB20
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Unimodal Probability Distributions for Deep Ordinal Classification
UnimodalProbabilityDistributionsforDeepOrdinalClassiﬁcationChristopherBeckham1ChristopherPal1Abstractotherclassesmaynotnecessarilymakesenseincontext.WepresentanexampleofthisinFigure1,showingthreeP...
for Deep Classification Ordinal Unimodal
2023-11-12 20:45:281914758.02 KB22
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Uniform Deviation Bounds for k-Means Clustering
UniformDeviationBoundsfork-MeansClusteringOlivierBachem1MarioLucic1S.HamedHassani1AndreasKrause1AbstractRelatedwork.TraditionalVapnik-Chervonenkistheoryprovidestoolstoobtainuniformdeviationboundsfo...
for Clustering k-Means bounds Uniform
2023-11-12 20:45:271241555.15 KB7
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Uniform Convergence Rates for Kernel Density Estimation
UniformConvergenceRatesforKernelDensityEstimationHeinrichJiang1Abstractboundedaswellasdecayassumptionsonthekernelfunc-tions.Moreover,theseboundsholduniformlyoverRdandKerneldensityestimation(KDE)isa...
for Kernel Convergence Density Estimation
2023-11-12 20:45:271798323.21 KB9
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Tight Bounds for Approximate Carathéodory and Beyond
TightBoundsforApproximateCarathéodoryandBeyondVahabMirrokni1RenatoPaesLeme1AdrianVladu2SamChiu-waiWong3Abstractversionstatesthatifoneiswillingtotolerateanerrorof✏in`norm,OD2p/✏2verticessufﬁceto...
for and Approximate bounds Tight
2023-11-12 20:45:241965755.03 KB26
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Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank
TheoreticalPropertiesforNeuralNetworkswithWeightMatricesofLowDisplacementRankLiangZhao1SiyuLiao1YanzhiWang2ZheLi2JianTang2BoYuan1AbstractFigure1.ExamplesofcommonlyusedLDR(structured)matri-ces,i.e.,...
Neural for Networks with Theoretical
2023-11-12 20:45:241387413.54 KB24
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