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Adaptive and Self-Confident On-Line Learning Algorithms
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Outgoing claims: the papers this work cites, builds on, or argues with.
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A Game of Prediction with Expert Advice
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Derandomizing Stochastic Prediction Strategies
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Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions
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Relative Loss Bounds for Multidimensional Regression Problems
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Worst-case quadratic loss bounds for prediction using linear functions and gradient descent
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Relative loss bounds for single neurons
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Additive versus exponentiated gradient updates for linear prediction
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Derandomizing stochastic prediction strategies
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General convergence results for linear discriminant updates
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The binary exponentiated gradient algorithm for learning linear functions
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The robustness of the p -norm algorithms
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Regret bounds for prediction problems
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Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms
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Aggregation of time series predictors, optimality in a locally stationary context
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Characterizing edges in signed and vector-valued graphs
inferred← citeshttps://doi.org/10.70675/95411dd9z65e2z492dz8df4za2a8f161491fopenalex-snapshot
Contributions à l'apprentissage statistique : estimation de densité, agrégation d'experts et forêts aléatoires
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Learning in Mean Field Games
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Sequential learning with similarities
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Online Learning with Kernels
inferred← citeshttps://doi.org/10.7916/d8jm2hm7openalex-snapshot
Essays on Inventory Management and Object Allocation
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