On the Universal Approximation Problem in the Generalized Hybrid Dimension


On the Universal Approximation Problem in the Generalized Hybrid Dimension – We explore the problem of learning linear classifiers for sparse input data, which is the task of learning a latent vector from an input vector of its labels. We show empirically that we can easily learn this representation from a small set of labeled data which is of low-rank. Furthermore, it is possible to learn the latent vector in general from small label sets. Finally, we illustrate the usefulness of this representation for various applications, such as clustering, classification and regression, in a single-label setting. The proposed algorithm is shown to be a very efficient representation of sparse data by directly incorporating label information into the learning algorithm.

One of the main challenges in multiagent optimization is to identify the optimal policies that can be optimized. In many real world applications, one can identify the optimal policy, or the policy is optimal when the system can be evaluated on a given set of constraints. In this paper, we provide a fast algorithm for optimization of policy policies under uncertain configurations. Our algorithm can be easily extended to the real world problem of evaluating policies defined in terms of a continuous state space, where the policy can be expressed either via the model or a nonlinear domain. Our algorithm, L0-QA, implements a family of optimization algorithms, named LQA, that achieves state-space optimization under discrete and continuous constraints.

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On the Universal Approximation Problem in the Generalized Hybrid Dimension

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  • Stochastic Gradient MCMC Methods for Nonconvex Optimization

    Dynamic Systems as a Multi-Agent SimulationOne of the main challenges in multiagent optimization is to identify the optimal policies that can be optimized. In many real world applications, one can identify the optimal policy, or the policy is optimal when the system can be evaluated on a given set of constraints. In this paper, we provide a fast algorithm for optimization of policy policies under uncertain configurations. Our algorithm can be easily extended to the real world problem of evaluating policies defined in terms of a continuous state space, where the policy can be expressed either via the model or a nonlinear domain. Our algorithm, L0-QA, implements a family of optimization algorithms, named LQA, that achieves state-space optimization under discrete and continuous constraints.


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