Showing 1–20 of 111 results
/ Date/ Name
Feb 14, 2012Ensembles of Kernel PredictorsMay 9, 2012L2 Regularization for Learning KernelsFeb 3, 2023Pseudonorm Approachability and Applications to Regret MinimizationMay 20, 2018Algorithms and Theory for Multiple-Source AdaptationFeb 23, 2021Learning with User-Level PrivacyOct 22, 2013Relative Deviation Learning Bounds and Generalization with Unbounded Loss FunctionsJul 21, 2020On the Rademacher Complexity of Linear Hypothesis SetsJun 26, 2020Relative Deviation Margin BoundsFeb 25, 2020Three Approaches for Personalization with Applications to Federated LearningAug 23, 2022A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement LearningJul 14, 2021A Field Guide to Federated OptimizationDec 10, 2019Advances and Open Problems in Federated LearningMar 2, 2012Algorithms for Learning Kernels Based on Centered AlignmentJun 8, 2015Non-parametric Revenue Optimization for Generalized Second Price AuctionsApr 28, 2020Adversarial Learning Guarantees for Linear Hypotheses and Neural NetworksAug 25, 2020A Discriminative Technique for Multiple-Source AdaptationOct 20, 2019Learning GANs and Ensembles Using DiscrepancyJul 2, 2021Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement LearningMay 4, 2021A Finer Calibration Analysis for Adversarial RobustnessApr 21, 2022Differentially Private Learning with Margin Guarantees