Conference Agenda
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Daily Overview |
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MDS: Mathematics of Data Science Location: A702 Session Chair: Lyudmila Grigoryeva Session Chair: Tobias Sutter | |
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Statistical Inference with (Shallow) Random Weights Neural Networks 1: University of St. Gallen, Switzerland; 2: Imperial College London; 3: University of Warwick Random Weights Neural Networks (RWNNs) have attracted significant attention in the literature and practical applications due to their ability to approximate complex functions with minimal and easy-to-implement training. Although prior research has established the universal approximation properties and generalization bounds of these methods, statistical inference has remained largely unexamined. Leveraging recent advances in approximation theory, we derive faster-than-standard Monte Carlo approximation rates in both L2 and expected supremum norms. We then establish asymptotic consistency and normality of RWNN estimators in L2 and uniform consistency under appropriate conditions. Our results highlight the interaction between randomness, regularization, and conditioning in determining the quality of the inference. | |
