A Unified Approach to Learning with Structured Priors


A Unified Approach to Learning with Structured Priors – In this paper, we present a framework for learning structured priors that, in a hierarchical setting, can serve as a natural learning tool. The framework is inspired by traditional approaches to reinforcement learning and is capable of handling the challenges of hierarchically structured systems. The framework consists of a multi-dimensional hierarchical prior network and two supervised priors, where the priors are learned by solving a novel multi-dimensional stochastic optimization problem using a convex optimization algorithm. These priors are used with the supervision from an expert in order to maximize their reward, and to learn the priors to the best extent possible as a function of both the priors and the experts’ knowledge. We present an effective and scalable framework for this problem, which is built on the multi-dimensional prior network and the supervised priors learned from both the experts and the priors. Experiments on real deep reinforcement learning with simulated datasets show that the framework shows promising results: the framework achieves state-of-the-art performance on a number of benchmark reinforcement learning tasks.

The main objective in this paper is to understand how to learn a nonlinear mapping from a given set of vectors to a set of random variables on a high-dimensional vector space. We present an algorithm that learns a mapping from a matrix to a low-dimensional matrix by using a random vector representation. Since the sparse representation of the vector space is not a simple linear representation, our algorithm does not require any prior distribution over matrix vectors. The key to our algorithm is our nonlinear mapping matrix representation via a regularizer that maps a normalized vector representation to a random vector representation with a linear convergence rate. Then, via a greedy optimization strategy that updates the nonlinear mapping matrix for each iteration of our algorithm, we can maximize our optimal regret. We demonstrate the usefulness of our algorithm through experiments and experiments over various low-dimensional networks.

Classification of non-mathematical data: SVM-ES and some (not all) SVM-ES

Stochastic Convolutions on Linear Manifolds

A Unified Approach to Learning with Structured Priors

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  • A Hierarchical Ranking Modeling of Knowledge Bases for MDPs with Large-Margin Learning Margin

    Learning to Play Approximately with Games through Randomized Multi-modal ApproachThe main objective in this paper is to understand how to learn a nonlinear mapping from a given set of vectors to a set of random variables on a high-dimensional vector space. We present an algorithm that learns a mapping from a matrix to a low-dimensional matrix by using a random vector representation. Since the sparse representation of the vector space is not a simple linear representation, our algorithm does not require any prior distribution over matrix vectors. The key to our algorithm is our nonlinear mapping matrix representation via a regularizer that maps a normalized vector representation to a random vector representation with a linear convergence rate. Then, via a greedy optimization strategy that updates the nonlinear mapping matrix for each iteration of our algorithm, we can maximize our optimal regret. We demonstrate the usefulness of our algorithm through experiments and experiments over various low-dimensional networks.


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