Machine learning and artificial intelligence (AI) are powerful tools that create predictive models, extract information, and help make complex decisions. They do this by examining an enormous quantity of labeled training data to find patterns too complex for human observation. However, in many real-world applications, well-labeled data can be difficult, expensive, or even impossible to obtain. In some cases, such as when identifying rare objects like new archeological sites or secret enemy military facilities in satellite images, acquiring labels could require months of trained human observers at incredible expense. Other times, as when attempting to predict disease infection during a pandemic such as COVID-19, reliable true labels may be nearly impossible to obtain early on due to lack of testing equipment or other factors. In that scenario, identifying even a small amount of truly negative data may be impossible due to the high false negative rate of available tests. In such problems, it is possible to label a small subset of data as belonging to the class of interest though it is impractical to manually label all data not of interest. We are left with a small set of positive labeled data and a large set of unknown and unlabeled data. Readers will explore this Positive and Unlabeled learning (PU learning) problem in depth. The book rigorously defines the PU learning problem, discusses several common assumptions that are frequently made about the problem and their implications, and considers how to evaluate solutions for this problem before describing several of the most popular algorithms to solve this problem. It explores several uses for PU learning including applications in biological/medical, business, security, and signal processing. This book also provides high-level summaries of several related learning problems such as one-class classification, anomaly detection, and noisy learning and their relation to PU learning.

The majority of information on the Internet is expressed in written text. Understanding and extracting this information is crucial to building intelligent systems that can organize this knowledge, but most algorithms focus on learning atomic facts and relations. For instance, we can reliably extract facts like "Stanford is a University" and "Professors teach Science" by observing redundant word patterns across a corpus. However, these facts do not capture richer knowledge like the way detonating a bomb is related to destroying a building, or that the perpetrator who was convicted must have been arrested. A structured model of these events and entities is needed to understand language across many genres, including news, blogs, and even social media. This dissertation describes a new approach to knowledge acquisition and extraction that learns rich structures of events (e.g., plant, detonate, destroy) and participants (e.g., suspect, target, victim) over a large corpus of news articles, beginning from scratch and without human involvement. As opposed to early event models in Natural Language Processing (NLP) such as scripts and frames, modern statistical approaches and advances in NLP now enable new representations and large-scale learning over many domains. This dissertation begins by describing a new model of events and entities called Narrative Event Schemas. A Narrative Event Schema is a collection of events that occur together in the real world, linked by the typical entities involved. I describe the representation itself, followed by a statistical learning algorithm that observes chains of entities repeatedly connecting the same sets of events within documents. The learning process extracts thousands of verbs within schemas from 14 years of newspaper data. I present novel contributions in the field of temporal ordering to build classifiers that order the events and infer likely schema orderings. I then present several new evaluations for the extracted knowledge. Finally, I apply Narrative Event Schemas to the field of Information Extraction, learning templates of events with sets of semantic roles. Most Information Extraction approaches assume foreknowledge of the domain's templates, but I instead start from scratch and learn schemas as templates, and then extract the entities from text as in a standard extraction task. My algorithm is the first to learn templates without human guidance, and its results approach those of supervised algorithms.

"One of the most provocative entrepreneurs of our time, who started Eckō Unltd out of his parents' garage and turned it into a media empire, Marc Eckō reveals his formula for building an authentic brand or business. Marc Eckō began his career by spray-painting t-shirts in the garage of his childhood home in suburban New Jersey. A graffiti artist with no connections and no fashion pedigree, he left the safety net of pharmacy school to start his own company. Armed with only hustle, sweat equity, and creativity, he flipped a $5,000 bag of cash into a global corporation now worth $500 million. Unlabel is a success story, but it's one that shares the bruises, scabs, and gut-wrenching mistakes that every entrepreneur must overcome to succeed. Through his personal prescription for success--the Authenticity Formula--Eckō recounts his many innovations and misadventures in his journey from misfit kid to the CEO. It wasn't a meteoric rise; in fact, it was a rollercoaster that dipped to the edge of bankruptcy and even to national notoriety, but this is an underdog story we can learn from: Ecko's doubling down on the core principles of the brand and his formula for action over talk are all lessons for today's entrepreneurs. Ecko offers a brash message with his inspirational story: embrace pain, take risks, and be yourself. Unlabel demonstrates that, like or not, you are a brand and it's up you to take control of it and create something authentic. Unlabel is a groundbreaking guide to channeling your creativity, finding the courage to defy convention, and summoning the confidence to act and be competitive in any environment"--