Learning to Rank for Passive Perception in Unlabeled Data – We propose a novel method for classification tasks, by first finding a score of items that the target (or a subset of items) is interested in. This is a very challenging task, and our method is motivated by the following question: how to predict the target of an item? The goal of this work is to infer its value of a set of items and use that value to generate a ranking metric. We propose an algorithm that learns a rank-based value which serves as a baseline to improve classification accuracy. The method is applied to two challenging categories, namely, text classification and video analysis. Our experiments demonstrate the effectiveness of using the rank-based value to improve classification performance.

Most of the existing literature is dominated by theoretical work where there are a lot of assumptions that can be made about the unknown distribution of the sample, leading to a significant amount of uncertainty. In this work we propose a novel method in which we are able to estimate the distributions of the data without any knowledge of the unknown distribution so that the model is not biased. The main contributions of this work are: 1) as the result of careful statistical modeling, we can learn an efficient estimation of the distribution parameters and hence provide a new general rule for modeling the random variable. 2) We demonstrate that in general the results obtained from our approach are not highly inaccurate due to the fact that they are not suitable as general rules. By analyzing the underlying assumptions and the uncertainty in the distribution, we derive a new general rule for modeling the random variables and provide new conditions under which we can avoid being biased. Finally, we study how these rules are interpreted by the model-driven decision-making agent, and show how to define a general rule for modeling the random variables.

The Sample-Efficient Analysis of Convexity of Bayes-Optimal Covariate Shift

# Learning to Rank for Passive Perception in Unlabeled Data

A deep residual network for event prediction

Modelling the Modal Rate of Interest for a Large Discrete Random VariableMost of the existing literature is dominated by theoretical work where there are a lot of assumptions that can be made about the unknown distribution of the sample, leading to a significant amount of uncertainty. In this work we propose a novel method in which we are able to estimate the distributions of the data without any knowledge of the unknown distribution so that the model is not biased. The main contributions of this work are: 1) as the result of careful statistical modeling, we can learn an efficient estimation of the distribution parameters and hence provide a new general rule for modeling the random variable. 2) We demonstrate that in general the results obtained from our approach are not highly inaccurate due to the fact that they are not suitable as general rules. By analyzing the underlying assumptions and the uncertainty in the distribution, we derive a new general rule for modeling the random variables and provide new conditions under which we can avoid being biased. Finally, we study how these rules are interpreted by the model-driven decision-making agent, and show how to define a general rule for modeling the random variables.