"User"的相关文档

Incentivized Bandit Learning with Self-Reinforcing User Preferences
IncentivizedBanditLearningwithSelf-ReinforcingUserPreferencesTianchenZhou1JiaLiu1ChaoshengDong2JingyuanDeng2Abstractaccumulatesmorepositivefeedbacks.Forexample,onamovierentalwebsite,currentcustomer...
Learning with Preferences Bandit User
2023-11-16 18:47:031052641.59 KB17
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Federated Learning of User Verification Models Without Sharing Embeddings
FederatedLearningofUserVeriﬁcationModelsWithoutSharingEmbeddingsHosseinHosseini1HyunsinPark1SungrackYun1ChristosLouizos1JosephSoriaga1MaxWelling1Abstractbeddingiscloseenoughtoareferenceembedding,a...
Learning of Models without Federated
2023-11-16 18:38:1015914.2 MB13
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Multi-Task Learning with User Preferences Gradient Descent with Controlled Ascent in Pareto Optimization
Multi-TaskLearningwithUserPreferences:GradientDescentwithControlledAscentinParetoOptimizationDebabrataMahapatra1VaibhavRajan2Abstract2019a),naturallanguageprocessing(Liuetal.,2019b)andbioinformatic...
Learning with Gradient Descent Preferences
2023-11-14 21:45:181620872.7 KB8
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Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
GenerativeAdversarialUserModelforReinforcementLearningBasedRecommendationSystemXinshiChen1†ShuangLi2HuiLi4ShaohuaJiang4YuanQi4LeSong34Abstractexplicitlytakeintoaccountthelong-termUserinterest.How-...
Learning for Adversarial Generative Reinforcement
2023-11-13 14:47:187491.46 MB29
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Bounding User Contributions A Bias-Variance Trade-off in Differential Privacy
BoundingUserContributions:ABias-VarianceTrade-offinDifferentialPrivacyKareemAmin1AlexKulesza1AndresMun˜ozMedina1SergeiVassilvitskii1AbstractthatthepresenceofanysingleUserinthedatasetcannotbeaccura...
in Differential Bounding User Contributions
2023-11-13 14:46:33597880.6 KB6
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Incentivized Bandit Learning with Self-Reinforcing User Preferences

Federated Learning of User Verification Models Without Sharing Embeddings

Multi-Task Learning with User Preferences Gradient Descent with Controlled Ascent in Pareto Optimization

Generative Adversarial User Model for Reinforcement Learning Based Recommendation System

Bounding User Contributions A Bias-Variance Trade-off in Differential Privacy