\setHeader{Friday Morning Tutorials}{Friday}{5}
%\pageTitle{Friday Morning Tutorials}

\tutorialHeader{PAC-Bayesian Analysis and Its Applications}
{Yevgeny Seldin, Fran\c{c}ois Laviolette and John Shawe-Taylor}
{10:30--13:00}{Room: Wills, G25}

\tutorialIntro{PAC-Bayesian analysis is a basic and very general tool for data-dependent analysis in machine learning. By now, it has been applied in such diverse areas as supervised learning, unsupervised learning, and reinforcement learning, leading to state-of-the-art algorithms and accompanying generalization bounds. PAC-Bayesian analysis, in a sense, takes the best out of Bayesian methods and PAC learning and puts it together: (1) it provides an easy way to exploit prior knowledge (like Bayesian methods); (2) it provides strict and explicit generalization guarantees (like VC theory); and (3) it is data-dependent and provides an easy and strict way of exploiting benign conditions (like Rademacher complexities). In addition, PAC-Bayesian bounds directly lead to efficient learning algorithms.

We will start with a general introduction to PAC-Bayesian analysis, which should be accessible to an average student, who is familiar with machine learning at the basic level. Then, we will survey multiple forms of PAC-Bayesian bounds and their numerous applications in different fields (including supervised and unsupervised learning, finite and continuous domains, and the very recent extension to martingales and reinforcement learning). Some of these applications will be explained in more details, while others will be surveyed at a high level. We will also describe the relations and distinctions between PAC-Bayesian analysis, Bayesian learning, VC theory, and Rademacher complexities. We will discuss the role, value, and shortcomings of frequentist bounds that are inspired by Bayesian analysis.

This tutorial builds upon a related tutorial given at ICML-2012. While there will naturally be certain overlap in the material, certain aspects will be presented differently and a stronger stress on applications will be provided.}


\tutorialHeader{Random Projections for Machine Learning and Data Mining: Theory and Applications} 
{Ata Kaban and Bob Durrant} {10:30--13:00}{Room: Wills, 3.31}

\tutorialIntro{Random projections have been used as a dimensionality reduction technique for large data since the appearance of Arriaga and Vempala's seminal FOCS 1999 paper, and they continue to find applications both within the field of Machine Learning and elsewhere. Starting with some motivating examples from Machine Learning and Data Mining, this tutorial will review some key theoretical properties of random projections, and the practical applications this theory has inspired. In particular, we will cover the Johnson-Lindenstrauss lemma which gives conditions under which random projections approximately preserve geometric properties of data and give related applications, discuss the field of Compressed Sensing from which one can derive guarantees for techniques working with sparse data which we illustrate in the setting of SVM, discuss more recent theory giving guarantees for linear classifiers and regressors working with non-sparse data, and finally we take a look at random projections as regularizers.}




\newpage
\setHeader{Friday Afternoon Tutorials}{Friday}{5}
%\pageTitle{Friday Afternoon Tutorials}

\tutorialHeader{Probabilistic Modeling of Ranking}
{Jose A.\ Lozano and Ekhi\~{n}e Irurozki}
{14:30--17:00}{Room: Wills, 3.31}

\tutorialIntro{Rankings and permutations have become, nowadays, ubiquitous. They appear in numerous areas of computer systems: information retrieval, recommender systems, identity tracking or chemical compound classification, etc. Dealing with rankings, and particularly with rankings of many objects is a complex computational task as the number of permutations of $n$ objects scales factorially in $n$. Recently a number of approaches have come to the machine learning arena to address this kind of data. Most of these approaches are based on the building of a probability distribution over the space of rankings. However, given the complexity of storing, learning and making inference on this kind of models, different simplifying assumptions have been considered: the use of parametric models, models based on low-order statistics, models based on kernels and the definition and use of notions of independence and conditional independence in the space of permutations. In this tutorial we will review the literature on the topic, explaining the different approaches in detail that have emerged in the literature, putting them in relation with other non-probabilistic ranking models and giving a collection of open problems in the area. In addition we will present the most relevant applications in the field as well as the most common benchmark datasets and software.}


\tutorialHeader{Understanding and Managing Cascades on Large Graphs}
{B.\ Aditya Prakash and Christos Faloutsos}
{14:30--17:00}{Room: Wills, G25}

\tutorialIntro{How do contagions spread in population networks? Which group should we market to, for maximizing product penetration? Will a given YouTube video go viral? Who are the best people to vaccinate? What happens when two products compete? The objective of this tutorial is to provide an intuitive and concise overview of most important theoretical results and algorithms to help us understand and manipulate such propagation-style processes on large networks. The tutorial will contain three parts: (a) Theoretical results on the behavior of fundamental models; (b) Scalable Algorithms for changing the behavior of these processes e.g., for immunization, marketing etc.; and (c) Empirical Studies of diffusion on blogs and on-line websites like Twitter. We finally conclude with future research directions. 
The problems we focus on are central in surprisingly diverse areas: from computer science and engineering, epidemiology and public health, product marketing to information dissemination. Our emphasis is on intuition behind each topic, and guidelines for the practitioner.}
