Learning Gaussian Graphical Models by Inverting – Given a network of latent variables we propose a non-local model that learns the model parameters from a source random variable in the latent space, without learning the other variables themselves. We show that this method achieves better state-of-the-art results compared to other methods that have a local model learning the model parameters based on a latent random variable as well as on a non-local model learning the model parameters, and the resulting model is better performing on real-world datasets.

We present an online learning approach for online learning in a hierarchical environment. The learning agent and the agent with the best prediction and rewards are both trained independently and simultaneously. We show how to train this network in a hierarchical environment that works in a nonlinear time-step. The agent’s objective is to minimize the expected reward with the greatest regret, and the agent’s goal over the training and its expected reward are two different types of regret, which are different from those in nonlinear-time-steps. The agent’s learning problem can be seen as a binary multi-stage problem, involving two interacting agents with one objective. The agent must generate an optimal reward with the highest possible regret over all actions, and when it finds that it is the optimal one, it will learn. We demonstrate how to use this problem to automatically predict the outcome of a decision under uncertainty. Our method provides a straightforward and efficient solution, which we demonstrate at a large scale in both synthetic and real-world applications.

Uncertainty Decomposition in Belief Propagation

The Weighted Mean Estimation for Gaussian Graphical Models with Linear Noisy Regression

# Learning Gaussian Graphical Models by Inverting

Constrained Learning and Learning: A Deep Reinforcement Learning ApproachWe present an online learning approach for online learning in a hierarchical environment. The learning agent and the agent with the best prediction and rewards are both trained independently and simultaneously. We show how to train this network in a hierarchical environment that works in a nonlinear time-step. The agent’s objective is to minimize the expected reward with the greatest regret, and the agent’s goal over the training and its expected reward are two different types of regret, which are different from those in nonlinear-time-steps. The agent’s learning problem can be seen as a binary multi-stage problem, involving two interacting agents with one objective. The agent must generate an optimal reward with the highest possible regret over all actions, and when it finds that it is the optimal one, it will learn. We demonstrate how to use this problem to automatically predict the outcome of a decision under uncertainty. Our method provides a straightforward and efficient solution, which we demonstrate at a large scale in both synthetic and real-world applications.