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Gaussian Processes for Machine Learning
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TLDR
The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.Abstract:
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.read more
Citations
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Entropy search for information-efficient global optimization
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DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
TL;DR: In this paper, a Deep Stochastic IOC RNN Encoderdecoder framework, DESIRE, is proposed to predict future locations of objects in multiple scenes by accounting for the multi-modal nature of the future prediction (i.e., given the same context, future may vary).
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Distinguishing cause from effect using observational data: methods and benchmarks
TL;DR: Empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions.
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Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium
TL;DR: In this article, the authors quantitatively estimate properties of the quark-gluon plasma created in ultrarelativistic heavy-ion collisions utilizing Bayesian statistics and a multiparameter model-to-data comparison.
Proceedings ArticleDOI
Active Learning with Gaussian Processes for Object Categorization
TL;DR: This work derives a novel active category learning method based on the probabilistic regression model, and shows that a significant boost in classification performance is possible, especially when the amount of training data for a category is ultimately very small.
References
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