Research: Optimal Convergence Rate

Optimal Convergence Rate for Convex Programming under the Poisson-Sparsis Operator

This paper uses a recent work of Fechner et al. on the use of polynomial-time, stochastic, and stochastic methods for solving a variety of combinatorial optimization problems. We consider a recently proposed model learning algorithm, namely, the Deep Belief Networks (DBCs), which aims at learning a set of beliefs from a subset of data and then learning a probability distribution for this subset. We show that such algorithms are very general and may be computationally efficient. We prove several bounds for the optimal convergence to the posterior. We also perform a review of the literature on this algorithm, and show that it is very suitable for various problems in the area.

Compositional Argumentation with Inter-rater Agreement
We present an online framework that generalizes the Markov Decision Process (MDP) to an online environment where we learn to use the inputs and evaluate their performance. The goal is to predict the response of the agent on each of the two inputs to the agent. Our framework, the Multidimensional Markov Decision Process (MDP), is a fully online model where we learn to predict when the agent will respond in real time. We have developed a neural network to learn to predict when the agent will respond, by learning the distribution of the input and the response variable. We have tested the framework on three public datasets, and evaluated on some real world settings.
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Structured Multi-Label Learning for Text Classification
This paper proposes a new method to classify a set of images into two groups, called pairwise multi-label. The proposed learning model, named Label-Label Multi-Label Learning (LML), encodes the visual features of each image into a set of labels and the labels, respectively. The main objective is to learn which labels are similar to the data. To this end, the LML model can be designed by taking the labels as inputs, and is trained by computing the joint ranking. Since labels have importance for the classification, we design a pairwise multi-label learning method. We develop a set of two LMLs, i.e., two multi-label datasets for ImageNet, VGGNet, and ImageNet, with a combination of deep CNN and deep latent space models. The learned networks are connected in the two networks by a dual manifold, and are jointly optimized by a neural network. Through simulation experiments, we demonstrate that the network's performance can be considerably improved compared to the prior state-of-the-art approaches and outperforms that of those using supervised learning.

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