Adversarial Machine Learning by Yevgeniy Vorobeychik
List of Figures.- Preface.- Acknowledgments.- Introduction.- Machine Learning Preliminaries.- Categories of Attacks on Machine Learning.- Attacks at Decision Time.- Defending Against Decision-Time Attacks.- Data Poisoning Attacks.- Defending Against Data Poisoning.- Attacking and Defending Deep Learning.- The Road Ahead.- Bibliography.- Authors' Biographies.- Index .-
Graph Representation Learning
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A Concise Introduction to Models and Methods for Automated Planning
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Learning with Support Vector Machines
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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
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Reasoning with Probabilistic and Deterministic Graphical Models
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Action Programming Languages
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Human Computation
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Learning and Decision-Making from Rank Data
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Explainable Human-AI Interaction
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Network Embedding
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Predicting Human Decision-Making
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Game Theory for Data Science
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Strategic Voting
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Statistical Relational Artificial Intelligence
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A Short Introduction to Preferences
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Case-Based Reasoning
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Representing and Reasoning with Qualitative Preferences
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Robot Learning from Human Teachers
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Visual Object Recognition
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General Game Playing
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Essential Principles for Autonomous Robotics
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Federated Learning
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Graph-Based Semi-Supervised Learning
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An Introduction to Constraint-Based Temporal Reasoning
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Intelligent Autonomous Robotics
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Answer Set Solving in Practice
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Representation Discovery using Harmonic Analysis
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Representations and Techniques for 3D Object Recognition and Scene Interpretation
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Introduction to Intelligent Systems in Traffic and Transportation
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Introduction to Symbolic Plan and Goal Recognition
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Judgment Aggregation
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Metric Learning
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Data Integration
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Trading Agents
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Introduction to Semi-Supervised Learning
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Transfer Learning for Multiagent Reinforcement Learning Systems
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Introduction to Graph Neural Networks
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Introduction to Logic Programming
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An Introduction to the Planning Domain Definition Language
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Lifelong Machine Learning, Second Edition
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Multi-Objective Decision Making
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Active Learning
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Planning with Markov Decision Processes
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Computational Aspects of Cooperative Game Theory
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Algorithms for Reinforcement Learning
Yevgeniy Vorobeychik is an Associate Professor of Computer Science and Engineering at Washington University in Saint Louis. Previously, he was an Assistant Professor of Computer Science at Vanderbilt University. Between 2008 and 2010, he was a post-doctoral research associate at the University of Pennsylvania Computer and Information Science department. He received Ph.D. (2008) and M.S.E. (2004) degrees in Computer Science and Engineering from the University of Michigan, and a B.S. degree in Computer Engineering from Northwestern University. His work focuses on game theoretic modeling of security and privacy, adversarial machine learning, algorithmic and behavioral game theory and incentive design, optimization, agent-based modeling, complex systems, network science, and epidemic control. Dr. Vorobeychik received an NSF CAREER award in 2017, and was invited to give an IJCAI-16 early career spotlight talk. He was nominated for the 2008 ACM Doctoral Dissertation Award and received honorable mention for the 2008 IFAAMAS Distinguished Dissertation Award.Murat Kantarcioglu is a Professor of Computer Science and Director of the UTD Data Security and Privacy Lab at The University of Texas at Dallas. Currently, he is also a visiting scholar at Harvard's Data Privacy Lab. He holds a B.S. in Computer Engineering from Middle East Technical University, and M.S. and Ph.D. degrees in Computer Science from Purdue University. Dr. Kantarcioglu's research focuses on creating technologies that can efficiently extract useful information from any data without sacrificing privacy or security. His research has been supported by awards from NSF, AFOSR, ONR, NSA, and NIH. He has published over 175 peer-reviewed papers. His work has been covered by media outlets such as The Boston Globe and ABC News, among others, and has received three best paper awards. He is also the recipient of various awards including NSF CAREER award, a Purdue CERIAS Diamond Award for academic excellence, the AMIA (American Medical Informatics Association) 2014 Homer R. Warner Award, and the IEEE ISI (Intelligence and Security Informatics) 2017 Technical Achievement Award presented jointly by IEEE SMC and IEEE ITS societies for his research in data security and privacy. He is also a Distinguished Scientist of ACM.
| SKU | Unavailable |
| ISBN 13 | 9783031004520 |
| ISBN 10 | 3031004523 |
| Title | Adversarial Machine Learning |
| Author | Yevgeniy Vorobeychik |
| Series | Synthesis Lectures On Artificial Intelligence And Machine Learning |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2018-08-08 |
| Number of pages | 152 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |