Learning And Optimization In The Face Of Data Perturbations
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Learning And Optimization In The Face Of Data Perturbations
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Author : Matthew James Staib
language : en
Publisher:
Release Date : 2020
Learning And Optimization In The Face Of Data Perturbations written by Matthew James Staib and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Many problems in the machine learning pipeline boil down to maximizing the expectation of a function over a distribution. This is the classic problem of stochastic optimization. There are two key challenges in solving such stochastic optimization problems: 1) the function is often non-convex, making optimization difficult; 2) the distribution is not known exactly, but may be perturbed adversarially or is otherwise obscured. Each issue is individually so challenging to warrant a substantial accompanying body of work addressing it, but addressing them simultaneously remains difficult. This thesis addresses problems at the intersection of non-convexity and data perturbations. We study the intersection of the two issues along two dual lines of inquiry: first, we build perturbation-aware algorithms with guarantees for non-convex problems; second, we seek to understand how data perturbations can be leveraged to enhance non-convex optimization algorithms. Along the way, we will study new types of data perturbations and seek to understand their connection to generalization.
Machine Learning Optimization And Big Data
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Author : Giuseppe Nicosia
language : en
Publisher: Springer
Release Date : 2017-12-19
Machine Learning Optimization And Big Data written by Giuseppe Nicosia and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-19 with Computers categories.
This book constitutes the post-conference proceedings of the Third International Workshop on Machine Learning, Optimization, and Big Data, MOD 2017, held in Volterra, Italy, in September 2017. The 50 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.
Machine Learning Optimization And Data Science
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Author : Giuseppe Nicosia
language : en
Publisher: Springer
Release Date : 2019-02-16
Machine Learning Optimization And Data Science written by Giuseppe Nicosia and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-16 with Computers categories.
This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018.The 46 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.
Optimization In Machine Learning And Applications
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Author : Anand J. Kulkarni
language : en
Publisher: Springer Nature
Release Date : 2019-11-29
Optimization In Machine Learning And Applications written by Anand J. Kulkarni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-29 with Technology & Engineering categories.
This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.
Machine Learning Optimization And Big Data
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Author : Panos M. Pardalos
language : en
Publisher: Springer
Release Date : 2016-12-24
Machine Learning Optimization And Big Data written by Panos M. Pardalos and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-24 with Computers categories.
This book constitutes revised selected papers from the Second International Workshop on Machine Learning, Optimization, and Big Data, MOD 2016, held in Volterra, Italy, in August 2016. The 40 papers presented in this volume were carefully reviewed and selected from 97 submissions. These proceedings contain papers in the fields of Machine Learning, Computational Optimization and DataScience presenting a substantial array of ideas, technologies, algorithms, methods and applications.
Machine Learning Optimization And Big Data
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Author : Panos Pardalos
language : en
Publisher: Springer
Release Date : 2016-01-05
Machine Learning Optimization And Big Data written by Panos Pardalos and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-05 with Computers categories.
This book constitutes revised selected papers from the First International Workshop on Machine Learning, Optimization, and Big Data, MOD 2015, held in Taormina, Sicily, Italy, in July 2015. The 32 papers presented in this volume were carefully reviewed and selected from 73 submissions. They deal with the algorithms, methods and theories relevant in data science, optimization and machine learning.
Deep Learning Techniques And Optimization Strategies In Big Data Analytics
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Author : J. Joshua Thomas
language : en
Publisher: Engineering Science Reference
Release Date : 2019-10-14
Deep Learning Techniques And Optimization Strategies In Big Data Analytics written by J. Joshua Thomas and has been published by Engineering Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with Big data categories.
"This book examines the application of artificial intelligence in machine learning, data mining in unstructured data sets or databases, web mining, and information retrieval"--
Perturbations Optimization And Statistics
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Author : Tamir Hazan
language : en
Publisher: MIT Press
Release Date : 2023-12-05
Perturbations Optimization And Statistics written by Tamir Hazan and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-05 with Computers categories.
A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.
An Introduction To Optimization With Applications In Machine Learning And Data Analytics
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Author : Jeffrey Paul Wheeler
language : en
Publisher: CRC Press
Release Date : 2023-12-07
An Introduction To Optimization With Applications In Machine Learning And Data Analytics written by Jeffrey Paul Wheeler and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-07 with Mathematics categories.
The primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors as well as for students of engineering, computer science, economics, statistics, and business. Optimization reaches into many different fields. This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications, but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course. The book contains many practical applications but also explains the mathematics behind the techniques, including stating definitions and proving theorems. Optimization techniques are at the heart of the first spam filters, are used in self-driving cars, play a great role in machine learning, and can be used in such places as determining a batting order in a Major League Baseball game. Additionally, optimization has seemingly limitless other applications in business and industry. In short, knowledge of this subject offers an individual both a very marketable skill set for a wealth of jobs as well as useful tools for research in many academic disciplines. Many of the problems rely on using a computer. Microsoft’s Excel is most often used, as this is common in business, but Python and other languages are considered. The consideration of other programming languages permits experienced mathematics and engineering students to use MATLAB® or Mathematica, and the computer science students to write their own programs in Java or Python.
Distributionally Robust Learning
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Author : Ruidi Chen
language : en
Publisher:
Release Date : 2020-12-23
Distributionally Robust Learning written by Ruidi Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-23 with Mathematics categories.