Neural Information Processing Systems

Series Editors: Kay Chen Tan & Dacheng Tao
Topics considered include all areas of machine learning, including but not limited to: Decision tree - Artificial neural networks - Kernel learning - Bayesian learning - Ensemble methods - Dimension reduction and metric learning - Reinforcement learning - Meta learning and learning to learn - Imitation learning - Computational learning theory - Probabilistic graphical models - Transfer learning -
Multi-view and multi-task learning - Graph neural networks - Generative adversarial networks - Federated learning.

This series includes monographs, introductory and advanced textbooks, and state-of-the-art collections.

Titles published | About the series, manuscript guidelines, proposal form  | Contact Springer editor Dr. Celine Lanlan Chang

Series Editors: Barry O'Sullivan & Michael Wooldridge

This series provides a publication and communication platform for all AI topics, including but not limited to: Knowledge representation - Automated reasoning and inference - Reasoning under uncertainty - Planning, scheduling, and problem solving - Cognition and AI - Search - Diagnosis - Constraint processing - Multi-agent systems - Game theory in AI - Machine learning - Deep learning - Reinforcement learning - Data mining - Natural language processing - Computer vision - Human interfaces - Intelligent robotics - Explanation generation - Ethics in AI - Fairness, accountability, and transparency in AI.

This series includes monographs, introductory and advanced textbooks, state-of-the-art collections, and handbooks.

Titles published | About the series, manuscript guidelines, proposal form  | Contact Springer editor Dr Celine Lanlan Chang

Series Editor: Sing Bing Kang

The series covers both theoretical and applied aspects of computer vision, and provides texts for students and senior researchers in topics including, but not limited to: Deep learning for vision applications - Computational photography - Biological vision - Image and video processing - Document analysis and character recognition - Biometrics - Multimedia - Virtual and augmented reality - Vision for graphics - Vision and language - Robotics.

Titles published | About the series, manuscript guidelines, proposal form  | Contact Springer editor Dr Celine Lanlan Chang

Series Editor: Oge Marques

Our goal is to build a series of state of the art books for the libraries of both machine learning scientists interested in applying the latest techniques to novel problems, and subject matter experts, technologists, or researchers interested in leveraging the latest advances in machine learning for developing solutions that work for practical problems. Our aim is to cover all topics of relevance for applied machine learning and artificial intelligence. The series will also publish in-depth analyses and studies of applications to real world problems in areas such as economics, social good, environmental sciences, transportation science, manufacturing and production planning, logistics and distribution, financial planning, structural optimization, water resource planning, network design, and computer games.
In addition, this series will aim to address the interests of a wide spectrum of practitioners, students and researchers alike who are interested in employing machine learning and AI methods in their respective domains. The scope will span the breadth of machine learning and AI as it pertains to all application areas both through books that address techniques specific to one application domain and books that show the applicability of different types of machine learning methods to a wide array of domains.

This series includes monographs, textbooks, edited volumes and reference books.

About the series, manuscript guidelines, proposal form  | Contact Springer editor Paul Drougas