We want to believe that serendipity brings us together, but is that just a myth? Mining the comedy of missed connections, THIS RANDOM WORLD asks the serious question of how often we travel parallel paths through the world without noticing. From an ailing woman who plans one final trip, to her daughter planning one great escape and her son falling prey to a prank gone wrong, this funny, intimate, and heartbreaking play explores the lives that may be happening just out of reach of our own.
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.
This book is about conformal prediction, an approach to prediction that originated in machine learning in the late 1990s. The main feature of conformal prediction is the principled treatment of the reliability of predictions. The prediction algorithms described — conformal predictors — are provably valid in the sense that they evaluate the reliability of their own predictions in a way that is neither over-pessimistic nor over-optimistic (the latter being especially dangerous). The approach is still flexible enough to incorporate most of the existing powerful methods of machine learning. The book covers both key conformal predictors and the mathematical analysis of their properties. Algorithmic Learning in a Random World contains, in addition to proofs of validity, results about the efficiency of conformal predictors. The only assumption required for validity is that of "randomness" (the prediction algorithm is presented with independent and identically distributed examples); in later chapters, even the assumption of randomness is significantly relaxed. Interesting results about efficiency are established both under randomness and under stronger assumptions. Since publication of the First Edition in 2005 conformal prediction has found numerous applications in medicine and industry, and is becoming a popular machine-learning technique. This Second Edition contains three new chapters. One is about conformal predictive distributions, which are more informative than the set predictions produced by standard conformal predictors. Another is about the efficiency of ways of testing the assumption of randomness based on conformal prediction. The third new chapter harnesses conformal testing procedures for protecting machine-learning algorithms against changes in the distribution of the data. In addition, the existing chapters have been revised, updated, and expanded.
#1 New York Times bestseller! Actress, producer, and parent Kristen Bell (The Good Place, Veronica Mars, Frozen) and creative director and parent Benjamin Hart have a new challenge for you and your kids: become a purple person by embracing what makes YOU special while finding common ground with those around you. What is a purple person? Great question. I mean, really great! Because purple people always ask really great questions. They bring their family, friends, and communities together, and they speak up for what’s right. They are kind and hardworking, and they love to laugh (especially at Grandpa’s funny noises)! A purple person is an everyday superhero! How do you become one? That’s the fun part! Penny Purple will lead you through the steps. Get ready to be silly, exercise your curiosity, use your voice, and be inspired. Looking to reach beyond the political divide of red and blue, Kristen Bell and Benjamin Hart have created a hilarious and joyous read-aloud that offers a wonderful message about embracing the things that bring us together as humans. This book will inspire a whole generation to paint the world purple!
Features full-color maps capturing the geographical and political boundaries of the nations of the world, along with providing information on cities and towns, cultural attractions, statistics, topographical features, and flags of all nations.
Privacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data analysis, privacy-preserving techniques are often required to reduce the possibility of identifying sensitive information about individuals. For example, in medical data, sensitive information can be the fact that a particular patient suffers from HIV. In spatial data, sensitive information can be a specific location of an individual. In web surfing data, the information that a user browses certain websites may be considered sensitive. Consider a dataset containing some sensitive information is to be released to the public. In order to protect sensitive information, the simplest solution is not to disclose the information. However, this would be an overkill since it will hinder the process of data analysis over the data from which we can find interesting patterns. Moreover, in some applications, the data must be disclosed under the government regulations. Alternatively, the data owner can first modify the data such that the modified data can guarantee privacy and, at the same time, the modified data retains sufficient utility and can be released to other parties safely. This process is usually called as privacy-preserving data publishing. In this monograph, we study how the data owner can modify the data and how the modified data can preserve privacy and protect sensitive information. Table of Contents: Introduction / Fundamental Concepts / One-Time Data Publishing / Multiple-Time Data Publishing / Graph Data / Other Data Types / Future Research Directions
"The idea for the book came initially from my research notes/ internal blog ... [which] deals mainly with the changing nature of investment strategy of major global funds, the aftermath of the global financial crisis, the impact of Brexit, the global position of London, the increasingly political nature of global capital flows into the sector and the industry's default position which is one of delusion and a hope against hope for soft landings... These issues are set against a background of just how the real estate capital market has evolved ... from a local and rather dozy industry to one of increasing sophistication. However, the underlying argument is that this apparent shift is superficial despite the hope that, somehow, bankers have learnt from the lessons of the past, that rating agencies and regulators do more than tick boxes, or investors cease their natural tendency to stray from the world they know best." Joe Valente, June 2018 All profits from this book will be donated to Bloodwise.
You can be greater than you know how to be. In a world where you are pushed to know more, this book will prove that your greatest asset is often not knowing. In a world where you are told it’s all in the planning, this book will encourage you to keep your cart ahead of your horse and allow your dreams to lead you. In a world where you are told you need to become someone, this book will show you that you already are someone. The author—the foremost accidental expert on this subject—is Talia Leman. A high school student. Runner in the rain. Science enthusiast. World changer. Random kid. Writing with infectious enthusiasm, humor, and resoluteness, she shares her secrets to being more than you know how to be, including Don’t Line Up Your Ducks, Sideways Is a Better Way to Go Forward, and Too Many Cooks Is the Right Number. In this unexpectedly poignant, strikingly honest, and informative guide, Talia Leman shows you how to make room for life’s surprises, demonstrating that everyone has what it takes to make a difference for anything that matters to them. A Random Book about the Power of ANYone will move you to rethink and reimagine what’s possible, one random inspiration, one random idea, one random person at a time.
• Starts from the basics, focusing less on proofs and the high-level math underlying regressions, and adopts an engaging tone to provide a text which is entirely accessible to students who don’t have a stats background • New chapter on integrity and ethics in regression analysis • Each chapter offers boxed examples, stories, exercises and clear summaries, all of which are designed to support student learning • Optional appendix of statistical tools, providing a primer to readers who need it • Code in R and Stata, and data sets and exercises in Stata and CSV, to allow students to practice running their own regressions • Author-created videos on YouTube • PPT lecture slides and test bank for instructors
