Distributionally Robust Learning
Author: Ruidi Chen
Publisher:
Published: 2020-12-23
Total Pages: 258
ISBN-13: 9781680837728
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Robust Optimization
Author: Aharon Ben-Tal
Publisher: Princeton University Press
Published: 2009-08-10
Total Pages: 565
ISBN-13: 1400831059
DOWNLOAD EBOOKRobust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.
Empirical Likelihood
Author: Art B. Owen
Publisher: CRC Press
Published: 2001-05-18
Total Pages: 322
ISBN-13: 1420036157
DOWNLOAD EBOOKEmpirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. It al
Algorithms of Intelligence: Exploring the World of Machine Learning
Author: Dr R. Keerthika
Publisher: Inkbound Publishers
Published: 2022-01-20
Total Pages: 224
ISBN-13: 8196822340
DOWNLOAD EBOOKDelve into the fascinating world of machine learning with this comprehensive guide, which unpacks the algorithms driving today's intelligent systems. From foundational concepts to advanced applications, this book is essential for anyone looking to understand the mechanics behind AI.
Science of Cyber Security
Author: Feng Liu
Publisher: Springer Nature
Published: 2019-12-06
Total Pages: 387
ISBN-13: 3030346374
DOWNLOAD EBOOKThis book constitutes the proceedings of the Second International Conference on Science of Cyber Security, SciSec 2019, held in Nanjing, China, in August 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 62 submissions. These papers cover the following subjects: Artificial Intelligence for Cybersecurity, Machine Learning for Cybersecurity, and Mechanisms for Solving Actual Cybersecurity Problems (e.g., Blockchain, Attack and Defense; Encryptions with Cybersecurity Applications).
Adversarial Machine Learning
Author: Anthony D. Joseph
Publisher: Cambridge University Press
Published: 2019-02-21
Total Pages: 341
ISBN-13: 1107043468
DOWNLOAD EBOOKThis study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.
Algorithmic Learning in a Random World
Author: Vladimir Vovk
Publisher: Springer Science & Business Media
Published: 2005-03-22
Total Pages: 344
ISBN-13: 9780387001524
DOWNLOAD EBOOKAlgorithmic 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.
Density Ratio Estimation in Machine Learning
Author: Masashi Sugiyama
Publisher: Cambridge University Press
Published: 2012-02-20
Total Pages: 343
ISBN-13: 0521190177
DOWNLOAD EBOOKThis book introduces theories, methods and applications of density ratio estimation, a newly emerging paradigm in the machine learning community.
Machine Learning and Data Science Blueprints for Finance
Author: Hariom Tatsat
Publisher: "O'Reilly Media, Inc."
Published: 2020-10-01
Total Pages: 432
ISBN-13: 1492073008
DOWNLOAD EBOOKOver the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
Algorithmic Aspects of Machine Learning
Author: Ankur Moitra
Publisher: Cambridge University Press
Published: 2018-09-27
Total Pages: 161
ISBN-13: 1107184584
DOWNLOAD EBOOKIntroduces cutting-edge research on machine learning theory and practice, providing an accessible, modern algorithmic toolkit.
