Preference Learning

This report documents the program and the outcomes of Dagstuhl Seminar 14101 “Preference Learning”. Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The motivation for this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies

Income Distribution and Poverty in the Republic of Haiti

After decades of stagnation and economic decline coupled with political upheavals, the Republic of Haiti is today the poorest nation in the Western Hemisphere and one of the poorest in the world. The present research reveals that this country is also where income is worst distributed in the most unequal region of the world, viz., Latin America and the Carribbean. We use the 2001 Haiti Living Conditions Survey for distributive analysis and poverty assessment to try to make manifest the potential links between household well-being and individual socio-economic characteristics. One particular finding is that access to land does not help the poor escape poverty. Complementary to the inequality and poverty profiles constructed herein, a relatively new methodology using weighted least squares for complex survey is adopted to additively decompose inequality by multiple factor components. Also, we estimate a polychotomous ordered logic to investigate the risk of being indigent or poor.Republic of Haiti, inequality, multiple factor components decomposition, poverty, stochastic dominance

Pseudo-Marginal Bayesian Inference for Gaussian Processes

The main challenges that arise when adopting Gaussian Process priors in probabilistic modeling are how to carry out exact Bayesian inference and how to account for uncertainty on model parameters when making model-based predictions on out-of-sample data. Using probit regression as an illustrative working example, this paper presents a general and effective methodology based on the pseudo-marginal approach to Markov chain Monte Carlo that efficiently addresses both of these issues. The results presented in this paper show improvements over existing sampling methods to simulate from the posterior distribution over the parameters defining the covariance function of the Gaussian Process prior. This is particularly important as it offers a powerful tool to carry out full Bayesian inference of Gaussian Process based hierarchic statistical models in general. The results also demonstrate that Monte Carlo based integration of all model parameters is actually feasible in this class of models providing a superior quantification of uncertainty in predictions. Extensive comparisons with respect to state-of-the-art probabilistic classifiers confirm this assertion.Comment: 14 pages double colum

An easy-to-hard learning paradigm for multiple classes and multiple labels

© 2017 Weiwei Liu, Ivor W. Tsang and Klaus-Robert Müller. Many applications, such as human action recognition and object detection, can be formulated as a multiclass classification problem. One-vs-rest (OVR) is one of the most widely used approaches for multiclass classification due to its simplicity and excellent performance. However, many confusing classes in such applications will degrade its results. For example, hand clap and boxing are two confusing actions. Hand clap is easily misclassified as boxing, and vice versa. Therefore, precisely classifying confusing classes remains a challenging task. To obtain better performance for multiclass classifications that have confusing classes, we first develop a classifier chain model for multiclass classification (CCMC) to transfer class information between classifiers. Then, based on an analysis of our proposed model, we propose an easy-to-hard learning paradigm for multiclass classification to automatically identify easy and hard classes and then use the predictions from simpler classes to help solve harder classes. Similar to CCMC, the classifier chain (CC) model is also proposed by Read et al. (2009) to capture the label dependency for multi-label classification. However, CC does not consider the order of difficulty of the labels and achieves degenerated performance when there are many confusing labels. Therefore, it is non-trivial to learn the appropriate label order for CC. Motivated by our analysis for CCMC, we also propose the easy-to-hard learning paradigm for multi-label classi cation to automatically identify easy and hard labels, and then use the predictions from simpler labels to help solve harder labels. We also demonstrate that our proposed strategy can be successfully applied to a wide range of applications, such as ordinal classi cation and relationship prediction. Extensive empirical studies validate our analysis and the e-ectiveness of our proposed easy-to-hard learning strategies

Implementation and reporting of causal mediation analysis in 2015: a systematic review in epidemiological studies

BACKGROUND: Causal mediation analysis is often used to understand the impact of variables along the causal pathway of an occurrence relation. How well studies apply and report the elements of causal mediation analysis remains unknown. METHODS: We systematically reviewed epidemiological studies published in 2015 that employed causal mediation analysis to estimate direct and indirect effects of observed associations between an exposure on an outcome. We identified potential epidemiological studies through conducting a citation search within Web of Science and a keyword search within PubMed. Two reviewers independently screened studies for eligibility. For eligible studies, one reviewer performed data extraction, and a senior epidemiologist confirmed the extracted information. Empirical application and methodological details of the technique were extracted and summarized. RESULTS: Thirteen studies were eligible for data extraction. While the majority of studies reported and identified the effects of measures, most studies lacked sufficient details on the extent to which identifiability assumptions were satisfied. Although most studies addressed issues of unmeasured confounders either from empirical approaches or sensitivity analyses, the majority did not examine the potential bias arising from the measurement error of the mediator. Some studies allowed for exposure-mediator interaction and only a few presented results from models both with and without interactions. Power calculations were scarce. CONCLUSIONS: Reporting of causal mediation analysis is varied and suboptimal. Given that the application of causal mediation analysis will likely continue to increase, developing standards of reporting of causal mediation analysis in epidemiological research would be prudent

Risk Aversion in International Relations Theory

|When international relations theorists use the concept of risk aversion, they usually cite the economics conception involving concave utility functions. However, concavity is meaningful only when the goal is measurable on an interval scale. International decisions are usually not of this type, so that many statements appearing in the literature are formally meaningless. Applications of prospect theory face this difficulty especially, as risk aversion and acceptance are at their center. This paper gives two definitions of risk attitude that do not require an interval scale. The second and more distinctive one uses the property of submodularity in place of concavity. R. D. Luce has devised a theory of choice with features of prospect theory but not requiring on an interval scale, and the second definition in combination with this theory yields the traditional claim that decision makers are risk-averse for gains and risk-seeking for losses.risk aversion, prospect theory, international relations, joint receipts, measurement theory.

Multilayer Networks

In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such "multilayer" features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks, and many others. We also survey and discuss existing data sets that can be represented as multilayer networks. We review attempts to generalize single-layer-network diagnostics to multilayer networks. We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions, and various types of dynamical processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure

Measuring Poverty as a Fuzzy and Multidimensional Concept: Theory and Evidence from the United Kingdom

Previous research shows that poor people define poverty not only in material terms, but also in psychological and social terms, though it has been consistently characterized by economic resources in social sciences. Using a method based on `fuzzy-set' theory can be uniquely placed to answer the question as it allows us not only to tackle the problem of arbitrary poverty line, but also to integrate multiple dimensions into one index in an intuitive way. It can avoid the problem of poverty line entirely by introducing the concept of `membership function' which represents a degree of inclusion in a fuzzy subgroup poor. I therefore argue that the fuzzy measures of poverty can be a strong multidimensional alternative for the measures centered around income. To support the argument, two crucial points are clarfied. Firstly, the difference between traditional measures and the fuzzy measures needs to be discussed further since the discussions on the new measures so far lean more toward the fresh insights from the measures, so that the distinction in policy-relevant information has not been emphasized enough. From the comparison, I present that the fuzzy measures can provide a richer description of the social phenomenon, enabling a more acceptable distinction between different sub-populations. Secondly, how the measures behave statistically should be considered in depth because one of the most frequent critiques for poverty measurements is that present methods depend too much on arbitrary decisions like setting a poverty line. Utilizing a Monte Carlo simulation, I find that the measures (Totally Fuzzy, Totally Fuzzy and Relative, and Integrated Fuzzy and Relative) acknowledge two points quite well: (i) poverty is a multidimensional concept, and (ii) the `poor' and `non-poor' are not two mutually exclusive sets and the distinction can be `fuzzy'. It also turns out that the sampling distribution of the fuzzy measures is well-behaved, and they are robust to arbitrary choice in the estimation as well as reliable with relatively small sample size. Besides, I show that they are robust to measurement errors. Finally, I investigate the identification performance of each measure and show that the measures have a strong consistency