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Learning Deep Architectures for AI
Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
The Algorithmic Foundations of Differential Privacy
The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the ...
Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.
Toeplitz and Circulant Matrices
The fundamental theorems on the asymptotic behavior of eigenvalues, inverses, and products of banded Toeplitz matrices and Toeplitz matrices with absolutely summable elements are derived in a tutorial manner. Mathematical elegance and generality are sacrificed for conceptual simplicity and insight in the hope of making these results available to engineers lacking either the background or endurance to attack the mathematical literature on the subject. By limiting the generality of the matrices considered, the essential ideas and results can be conveyed in a more intuitive manner without the mathematical machinery required for the most general cases. As an application the results are applied t...
Proximal Algorithms
Proximal Algorithms discusses proximal operators and proximal algorithms, and illustrates their applicability to standard and distributed convex optimization in general and many applications of recent interest in particular. Much like Newton's method is a standard tool for solving unconstrained smooth optimization problems of modest size, proximal algorithms can be viewed as an analogous tool for nonsmooth, constrained, large-scale, or distributed versions of these problems. They are very generally applicable, but are especially well-suited to problems of substantial recent interest involving large or high-dimensional datasets. Proximal methods sit at a higher level of abstraction than class...
Information Theory and Statistics
Information Theory and Statistics: A Tutorial is concerned with applications of information theory concepts in statistics, in the finite alphabet setting. The topics covered include large deviations, hypothesis testing, maximum likelihood estimation in exponential families, analysis of contingency tables, and iterative algorithms with an "information geometry" background. Also, an introduction is provided to the theory of universal coding, and to statistical inference via the minimum description length principle motivated by that theory. The tutorial does not assume the reader has an in-depth knowledge of Information Theory or statistics. As such, Information Theory and Statistics: A Tutorial, is an excellent introductory text to this highly-important topic in mathematics, computer science and electrical engineering. It provides both students and researchers with an invaluable resource to quickly get up to speed in the field.
Entrepreneurship, Social Capital and Governance
This book highlights the role of entrepreneurship, social capital and governance for regional economic development. In recent decades, many researchers have claimed that entrepreneurship is the most critical factor in sustaining regional economic growth. However, most entrepreneurship research is undertaken without considering the fundamental importance of the regional context. Other research has emphasized the role of social capital but there are substantial problems in empirically relating measures of social capital to regional economic development. The expert contributors to this work highlight the role of governance in regional growth, an area that has so far been relatively under-resear...
Entrepreneurship Ecosystems and Their Opportunities and Challenges
Entrepreneurs are exceptionally gifted individuals capable of spotting projects, marshaling resources, inventing ideas, taking risks, and forming businesses. Prospective entrepreneurs must be inspired and motivated to pursue self-employment businesses in today’s volatile business environment and highly sophisticated information technologies. However, governments have been attempting to promote entrepreneurship by assisting the growth of small and medium-sized enterprises (SMEs). SMEs’ primary challenges are lack of working capital and marketing challenges. Entrepreneurship is a combination of difficult-to-teach skills, attitudes, and knowledge that can be developed. Entrepreneurship Ecos...
Global Entrepreneurship and Development Index 2017
This brief presents a detailed look at the entrepreneurial ecosystem of nations around the wold by combining individual data with institutional components. Presenting data from the 2017 Global Entrepreneurship and Development Index (GEDI), which measures the quality and scale of entrepreneurial process from 137 countries world-wide, this book provides a rich understanding of entrepreneurship and a more precise means to measure it. In addition to yearly data and comparison, this 2017 edition also explores the digital entrepreneurial ecosystem and provides a detailed analysis of two measurements of entrepreneurship: the GEDI and the Total Early-Stage Entrepreneurial Activity (TEA) measure. Whe...
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
In this monograph, the focus is on two extreme cases in which the analysis of regret is particularly simple and elegant: independent and identically distributed payoffs and adversarial payoffs. Besides the basic setting of finitely many actions, it analyzes some of the most important variants and extensions, such as the contextual bandit model.
