The necessary and sufficient conditions for existence of a generalized representer theorem are presented for learning Hilbert space - valued functions. Representer theorems involving explicit basis functions and Reproducing Kernels are a common occurrence in various machine learning algorithms like generalized least squares, support vector machines, Gaussian process regression, and kernel-based deep neural networks to name a few. Due to the more general structure of the underlying variational problems, the theory is also relevant to other application areas like optimal control, signal processing and decision making. The following presents a generalized representer theorem using the theory of closed, densely defined linear operators and subspace valued maps as a means to address variational optimization problems in learning and control. The implications of the theorem are presented with examples of multi-input - multi-output problems from kernel-based deep neural networks, stochastic regression and sparsity learning problems.