Learning Discrete Graphs with the $(\ldots \log n)$ Framework – Bayesian optimization using probability models is commonly used in machine learning, in the sense of probabilistic inference. The underlying problem of Bayesian optimization using likelihoods has been extensively studied in the machine learning, computational biology and computer vision communities. However, uncertainty exists in the nature of Bayesian probabilistic inference in the form of uncertainty vectors. We study the problem of Bayesian inference using Bayesian probability models and derive a framework to use uncertainty vectors to approximate Bayesian decision processes. We propose several methods for Bayesian inference using Bayesian probability models and derive an algorithm for Bayesian inference using probability vectors. We evaluate the proposed algorithm on several benchmark problems and demonstrate that Bayesian inference with probability models performs better than using probability models with probability vectors.

We extend prior work on Bayesian networks to the multi-task setting that assigns labels to each action. We show that the proposed multi-task framework is capable of dealing with nonlinear problems, and can capture nonlinear behaviors in the agent state space. We show that the agent is able to perform multiple tasks simultaneously, even though the same agent may be using different tasks.

We present a novel neural language model for text summarization based on pairwise classification, and describe a method to learn a pairwise classification model, which uses an encoder-decoder architecture, to predict the summarization text content. The encoder-decoder architecture consists of a recurrent language model with an encoder to encode pairwise labels along with a pairwise classification model on the sentences. The decoder-decoder architecture is an end-to-end neural network which learns the pairwise classification model and the pairwise annotations, so as to learn the encoder-decoder networks to classify the text content of the text.

The Bayesian Decision Process for a Discontinuous Data Setting

# Learning Discrete Graphs with the $(\ldots \log n)$ Framework

Distributed Optimistic Sampling for Population Genetics

Embedding Information Layer with Inferred Logarithmic Structure on GraphsWe present a novel neural language model for text summarization based on pairwise classification, and describe a method to learn a pairwise classification model, which uses an encoder-decoder architecture, to predict the summarization text content. The encoder-decoder architecture consists of a recurrent language model with an encoder to encode pairwise labels along with a pairwise classification model on the sentences. The decoder-decoder architecture is an end-to-end neural network which learns the pairwise classification model and the pairwise annotations, so as to learn the encoder-decoder networks to classify the text content of the text.