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Synthesis Lectures on Artifici...How is it possible to allow multiple data owners to collaboratively train and ... How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location typically... more
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Synthesis Lectures on Artifici...Learning to solve sequential decision-making tasks is difficult. Humans take y... Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason solve difficult tasks and collaborate with other humans towards a common goal.... more
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Synthesis Lectures on Artifici...Similarity between objects plays an important role in both human cognitive pro... Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many... more
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Synthesis Lectures on Artifici...Lifelong Machine Learning Second Edition is an introduction to an advanced mac... Lifelong Machine Learning Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast the current dominant... more
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Synthesis Lectures on Artifici...The increasing abundance of large high-quality datasets combined with signific... The increasing abundance of large high-quality datasets combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision language finance... more
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Synthesis Lectures on Artifici...The key idea behind active learning is that a machine learning algorithm can p... The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose queries usually in the form of unlabeled data... more
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Synthesis Lectures on Artifici...Reinforcement learning is a learning paradigm concerned with learning to contr... Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is... more
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Synthesis Lectures on Artifici...Support Vectors Machines have become a well established tool within machine le... Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits to face identification text... more
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Synthesis Lectures on Artifici...Reinforcement learning is a powerful tool in artificial intelligence in which ... Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals. In some cases this machine learning approach can save programmers time... more
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Synthesis Lectures on Artifici...The ubiquitous challenge of learning and decision-making from rank data arises... The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans learn from the data and then use the data to help humans make efficient effective... more
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Synthesis Lectures on Artifici...Semi-supervised learning is a learning paradigm concerned with the study of ho... Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally learning has been studied either in the unsupervised... more
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Synthesis Lectures on Artifici...Learning from Demonstration (LfD) explores techniques for learning a task poli... Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years with a wide variety of approaches for... more
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Synthesis Lectures on Artifici...Graph-structured data is ubiquitous throughout the natural and social sciences... Graph-structured data is ubiquitous throughout the natural and social sciences from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn... more
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Synthesis Lectures on Artifici...Machine learning and artificial intelligence (AI) are powerful tools that crea... 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... more
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Synthesis Lectures on Artifici...Multiagent systems is an expanding field that blends classical fields like gam... Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject covering... more
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