Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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High-dimensional statistics and learning Location: 0.004 Session Chair: Martin Wahl | |
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Self-regularized learning methods University of Stuttgart, Germany We introduce a new framework for the theoretical analysis of learning algorithms called self-regularization. In a nutshell, self-regularized learning algorithms guarantee implicitly that they produce sufficiently regular prediction functions. Central examples of self-regularized learning algorithms include gradient descent and regularized empirical risk minimization. We establish a general theory for the statistical analysis of self-regularized algorithms which in many cases yields minmax-optimal learning rates. Max Schölpple, Ingo Steinwart Institut für Stochastik und Anwendungen, Universität Stuttgart, Pfaffenwaldring 57, | |
