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Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel



Dominic Richards , Ilja Kuzborskij


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On the Role of Optimization in Double Descent: A Least Squares Study



Ilja Kuzborskij , Csaba Szepesvári , Omar Rivasplata , Amal Rannen-Triki , Razvan Pascanu


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Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping



Ilja Kuzborskij , Csaba Szepesvári

* COLT 2021 

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On Optimality of Meta-Learning in Fixed-Design Regression with Weighted Biased Regularization



Mikhail Konobeev , Ilja Kuzborskij , Csaba Szepesvári


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PAC-Bayes Analysis Beyond the Usual Bounds



Omar Rivasplata , Ilja Kuzborskij , Csaba Szepesvari , John Shawe-Taylor

* Enhanced version of the paper with the same title presented at the NeurIPS 2019 Workshop on Machine Learning with Guarantees. Important update: the PAC-Bayes bound for unbounded loss functions (Section 2.3) is new 

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Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting



Ilja Kuzborskij , Claire Vernade , András György , Csaba Szepesvári


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Locally-Adaptive Nonparametric Online Learning



Ilja Kuzborskij , Nicolò Cesa-Bianchi


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Efron-Stein PAC-Bayesian Inequalities



Ilja Kuzborskij , Csaba Szepesvári


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Distribution-Dependent Analysis of Gibbs-ERM Principle



Ilja Kuzborskij , Nicolò Cesa-Bianchi , Csaba Szepesvári


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Efficient Linear Bandits through Matrix Sketching



Ilja Kuzborskij , Leonardo Cella , Nicolò Cesa-Bianchi


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