Cortes, C., Mohri, M., and Rostamizadeh, A. New generalization bounds for learning kernels. International Conference on Machine Learning (ICML), 2010.Cortes, C., Mohri, M., Rostamizadeh, A.: Generalization bound
Carleo G, Cirac I, Cranmer K, Daudet L, Schuld M, Tishby N, Zdeborová L (2019) Machine learning and the physical sciences. Rev Mod Phys 91(4):045002 Google Scholar Caro M, Gur T, Rouzé C, Franca DS, Subramanian S (2023) Information-theoretic generalization bounds for learning from...
We further extend these results to the case where non-linear investment policies are considered using a kernel operator and show that with radial basis function kernels the performance guarantees become insensitive to how much side information is used. Finally, we illustrate our findings with a set...
Later, we will observe that the mathematical description of rotation invariant kernels on isotropic distributions reduces to this simple model in each learning stage. In this model, the kernel eigenvalues are equal \({\eta }_{\rho }=\frac{1}{N}\) for a finite number of modes ρ = ...
Learning theory is rich in bounds that have been derived and that relate quantities such as the empirical error, the true error probability, the number of training vectors, and the VC dimension or a VC related quantity. In his elegant theory of learning, Valiant [Vali 84] proposed to express...
Achieving small prediction errorR(α) is the ultimate goal of (quantum) machine learning. AsPis generally not known, the training errorR^S(α)is often taken as a proxy forR(α). This strategy can be justified via bounds on thegeneralization error...
Learning theory is rich in bounds that have been derived and that relate quantities such as the empirical error, the true error probability, the number of training vectors, and the VC dimension or a VC related quantity. In his elegant theory of learning, Valiant [Vali 84] proposed to express...
For both datasets, most of the Gaussian kernels yield smaller upper bounds on generalization error Full size table Fig. 2 Experimental test errors and accuracy on the test set at the different steps of the Gradient Descent optimization algorithm for first two classes of CIFAR-10 dataset Full ...
For the BLSTM, multiple ReLU activated hidden layers are used. The output of the network of the SMILES last character at the last layer is the output of the feature extractor. For the 1D-CNN, there are also multiple ReLU-activated hidden convolution layers. Each layer has kernels of size ...
Journal of Machine Learning Research, 12(Jul):2121–2159, 2011. 5 [17] Gintare Karolina Dziugaite and Daniel M Roy. Computing nonvacuous generalization bounds for deep (stochastic) neu- ral networks with many more parameters than training data. ...