Skip to main content Accessibility help
×
  1. Home
  2. Browse subjects
  3. Computer Science
  4. Content listing

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

48501 results in Computer Science

Page 1 of 2426

Accelerating Deep Neural Networks
  • Ryoma Sato
  • Coming soon
  • Expected online publication date:
    May 2026
    Print publication:
    31 May 2026

Inference in Statistical Modelling and Machine Learning

All of Regression

Markov Decision Processes and Reinforcement Learning
  • Martin L. Puterman, Timothy C. Y. Chan
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    30 April 2026
  • This book offers a comprehensive introduction to Markov decision process and reinforcement learning fundamentals using common mathematical notation and language. Its goal is to provide a solid foundation that enables readers to engage meaningfully with these rapidly evolving fields. Topics covered include finite and infinite horizon models, partially observable models, value function approximation, simulation-based methods, Monte Carlo methods, and Q-learning. Rigorous mathematical concepts and algorithmic developments are supported by numerous worked examples. As an up-to-date successor to Martin L. Puterman's influential 1994 textbook, this volume assumes familiarity with probability, mathematical notation, and proof techniques. It is ideally suited for students, researchers, and professionals in operations research, computer science, engineering, and economics.

The Cambridge Handbook of Behavioural Data Science
  • Edited by Ganna Pogrebna, Thomas T. Hills
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    30 April 2026
  • The Cambridge Handbook of Behavioural Data Science offers an essential exploration of how behavioural science and data science converge to study, predict, and explain human, algorithmic, and systemic behaviours. Bringing together scholars from psychology, economics, computer science, engineering, and philosophy, the Handbook presents interdisciplinary perspectives on emerging methods, ethical dilemmas, and real-world applications. Organised into modular parts-Human Behaviour, Algorithmic Behaviour, Systems and Culture, and Applications—it provides readers with a comprehensive, flexible map of the field. Covering topics from cognitive modelling to explainable AI, and from social network analysis to ethics of large language models, the Handbook reflects on both technical innovations and the societal impact of behavioural data, and reinforces concepts in online supplementary materials and videos. The book is an indispensable resource for researchers, students, practitioners, and policymakers who seek to engage critically and constructively with behavioural data in an increasingly digital and algorithmically mediated world.

Games on Graphs
  • From Logic and Automata to Algorithms
  • Edited by ‪Nathanaël Fijalkow
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    30 April 2026
  • This volume introduces the fundamental results and the state of the art in infinite duration games on graphs. These versatile game models first appeared in the study of automata and logic, but later became important for program verification and synthesis. They have many more applications. In particular, this book covers models from the fields of optimisation, reinforcement learning, model theory, and set theory. The material is presented at a level accessible to beginning graduate students, requiring only a basic understanding of discrete algorithms and graphs. The book's primary objective is constructing efficient algorithms for analysing different types of games. Rather than describe their implementation in full detail, it investigates their theoretical foundations and proves their properties and winning strategies. This tutorial reference from experts across the field is ideal for students, researchers, and professionals in logic and automata, verification and synthesis, and related fields.

The Cambridge Handbook of AI in Civil Dispute Resolution

The Cambridge Handbook of AI and Technologies in Courts

Basic Graph Theory
  • Béla Bollobás, Robert Morris
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    30 April 2026
  • Over the past few decades, graph theory has developed into one of the central areas of modern mathematics, with close (and growing) connections to areas of pure mathematics such as number theory, probability theory, algebra and geometry, as well as to applied areas such as the theory of networks, machine learning, statistical physics, and biology. It is a young and vibrant area, with several major breakthroughs having occurred in just the past few years. This book offers the reader a gentle introduction to the fundamental concepts and techniques of graph theory, covering classical topics such as matchings, colourings and connectivity, alongside the modern and vibrant areas of extremal graph theory, Ramsey theory, and random graphs. The focus throughout is on beautiful questions, ideas and proofs, and on illustrating simple but powerful techniques, such as the probabilistic method, that should be part of every young mathematician's toolkit.

Differential Equations and Variational Methods on Graphs
  • With Applications to Machine Learning and Image Analysis
  • Yves van Gennip, Jeremy Budd
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    31 March 2026
  • The burgeoning field of differential equations on graphs has experienced significant growth in the past decade, propelled by the use of variational methods in imaging and by its applications in machine learning. This text provides a detailed overview of the subject, serving as a reference for researchers and as an introduction for graduate students wishing to get up to speed. The authors look through the lens of variational calculus and differential equations, with a particular focus on graph-Laplacian-based models and the graph Ginzburg-Landau functional. They explore the diverse applications, numerical challenges, and theoretical foundations of these models. A meticulously curated bibliography comprising approximately 800 references helps to contextualise this work within the broader academic landscape. While primarily a review, this text also incorporates some original research, extending or refining existing results and methods.

Games, Markets, and Online Learning
  • Christian Kroer
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    31 March 2026
  • This book presents a modern introduction to the field of algorithmic game theory. It places a heavy emphasis on optimization and online learning (a subdiscipline of machine learning), which are tools that increasingly play a central role in both the theory and practice of applying game-theoretic ideas. The book covers the core techniques used in several majorly successful applications, including techniques used for creating superhuman poker AIs, the theory behind the 'pacing' methodology that has become standard in the internet advertising industry, and the application of competitive equilibrium from equal incomes for fair course seat allocation in many business schools. With its focus on online learning tools, this book is an ideal companion to classic texts on algorithmic game theory for graduate students and researchers.

Governing AI
  • A Primer
  • Onur Bakiner
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    31 March 2026
  • Governing AI is about getting AI right. Building upon AI scholarship in science and technology studies, technology law, business ethics, and computer science, it documents potential risks and actual harms associated with AI, lists proposed solutions to AI-related problems around the world, and assesses their impact. The book presents a vast range of theoretical debates and empirical evidence to document how and how well technical solutions, business self-regulation, and legal regulation work. It is a call to think inside and outside the box. Technical solutions, business self-regulation, and especially legal regulation can mitigate and even eliminate some of the potential risks and actual harms arising from the development and use of AI. However, the long-term health of the relationship between technology and society depends on whether ordinary people are empowered to participate in making informed decisions to govern the future of technology – AI included.

A Science of Concurrent Programs
  • Leslie Lamport
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    31 March 2026
  • Turing Award-winner Leslie Lamport shares the key lessons he has learned about concurrent and distributed computing over decades of writing and reasoning about their algorithms. Algorithms are not programs, and they shouldn't be written in a programming language. Instead, this book explores how to write them and reason about them by using mathematics. It explains the principles underlying abstract programs, which should be implemented to avoid concurrency errors. Designing an abstract program before writing any code can lead to better, more reliable programs. The book has very few mathematical prerequisites, with an appendix summarizing the necessary knowledge. The examples are written in TLA+ and are available online. This is a fascinating read for any graduate students and researchers in theoretical computer science, concurrency, and distributed systems.

Speech Technology
  • A Theoretical and Practical Introduction
  • Michael Hammond
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    31 March 2026
  • In recent years, speech recognition devices have become central to our everyday lives. Systems such as Siri, Alexa, speech-to-text, and automated telephone services, are built by people applying expertise in sound structure and natural language processing to generate computer programmes that can recognise and understand speech. This exciting new advancement has led to a rapid growth in speech technology courses being added to linguistics programmes; however, there has so far been a lack of material serving the needs of students who have limited or no background in computer science or mathematics. This textbook addresses that need, by providing an accessible introduction to the fundamentals of computer speech synthesis and automatic speech recognition technology, covering both neural and non-neural approaches. It explains the basic concepts in non-technical language, providing step-by-step explanations of each formula, practical activities and ready-made code for students to use, which is also available on an accompanying website.

Bandit Convex Optimisation
  • Tor Lattimore
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    28 February 2026
  • This comprehensive reference brings readers to the frontier of research on bandit convex optimization or zeroth-order convex optimization. The focus is on theoretical aspects, with short, self-contained chapters covering all the necessary tools from convex optimization and online learning, including gradient-based algorithms, interior point methods, cutting plane methods and information-theoretic machinery. The book features a large number of exercises, open problems and pointers to future research directions, making it ideal for students as well as researchers.

Extremal Graph and Hypergraph Theory
  • With Ramsey Theory
  • Dhruv Mubayi, Jacques A. Verstraete
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    28 February 2026
  • Providing a cohesive reference for advanced undergraduates, graduate students and even experienced researchers, this text contains both introductory and advanced material in extremal graph theory, hypergraph theory and Ramsey theory. Along the way, the book includes many modern proof techniques in the field such as the probabilistic method and algebraic methods. Several recent breakthroughs are presented with complete proofs, for example, recent results on the sunflower problem, and off-diagonal and geometric Ramsey theory. It is perhaps unique in containing material on both hypergraph regularity and containers. Featuring an extensive list of exercises, the text is suitable as a teaching text for a variety of courses in extremal combinatorics. Each of the two parts can form the basis of separate courses, and the majority of sections are designed to match the length of a single lecture.

Knowledge-Infused Learning
  • Neurosymbolic AI for Explainability, Interpretability, and Safety
  • Manas Gaur, Amit P. Sheth
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    28 February 2026
  • Knowledge-infused learning directly confronts the opacity of current 'black-box' AI models by combining data-driven machine learning techniques with the structured insights of symbolic AI. This guidebook introduces the pioneering techniques of neurosymbolic AI, which blends statistical models with symbolic knowledge to make AI safer and user-explainable. This is critical in high-stakes AI applications in healthcare, law, finance, and crisis management. The book brings readers up to speed on advancements in statistical AI, including transformer models such as BERT and GPT, and provides a comprehensive overview of weakly supervised, distantly supervised, and unsupervised learning methods alongside their knowledge-enhanced variants. Other topics include active learning, zero-shot learning, and model fusion. Beyond theory, the book presents practical considerations and applications of neurosymbolic AI in conversational systems, mental health, crisis management systems, and social and behavioral sciences, making it a pragmatic reference for AI system designers in academia and industry.

Computable Structure Theory
  • Beyond the Arithmetic
  • Antonio Montalbán
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    28 February 2026
  • Computable structure theory quantifies and studies the relative complexity of mathematical structures. This text, in conjunction with the author's previous volume, represents the first full monograph on computable structure theory in two decades. It brings new results of the author together with many older results that were previously scattered across the literature and presents them all in a coherent framework. Geared towards graduate students and researchers in mathematical logic, the book enables the reader to learn all the main results and techniques in the area for application in their own research. While the previous volume focused on countable structures whose complexity can be measured within arithmetic, this second volume delves into structures beyond arithmetic, moving into the realm of the hyperarithmetic and the infinitary languages.

Move Slow and Upgrade
  • The Power of Incremental Innovation
  • Evan Selinger, Albert Fox Cahn
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    05 February 2026
  • For far too long, tech titans peddled promises of disruptive innovation - fabricating benefits and minimizing harms. The promise of quick and easy fixes overpowered a growing chorus of critical voices, driving a sea of private and public investments into increasingly dangerous, misguided, and doomed forms of disruption, with the public paying the price. But what's the alternative? Upgrades - evidence-based, incremental change. Instead of continuing to invest in untested, high-risk innovations, constantly chasing outsized returns, upgraders seek a more proven path to proportional progress. This book dives deep into some of the most disastrous innovations of recent years - the metaverse, cryptocurrency, home surveillance, and AI, to name a few - while highlighting some of the unsung upgraders pushing real progress each day. Timely and corrective, Move Slow and Upgrade pushes us past the baseless promises of innovation, towards realistic hope.

One Hundred Years of Game Theory
  • A Nobel Symposium
  • Edited by Mark Voorneveld, Jörgen W. Weibull, Tommy Andersson, Roger Myerson, Jean-François Laslier, Rida Laraki, Yukio Koriyama
  • Coming soon
  • Expected online publication date:
    January 2026
    Print publication:
    31 January 2026
  • In the winter of 2021, the Swedish Nobel Foundation organized a Nobel symposium 'One Hundred Years of Game Theory' to commemorate the publication of famous mathematician Emile Borel's 'La théorie du jeu et les équations intégrales à noyau symétrique'. The symposium gathered roughly forty of the world's most prominent scholars ranging from mathematical foundations to applications in economics, political science, computer science, biology, sociology, and other fields. One Hundred Years of Game Theory brings together their writings to summarize and put in perspective the main achievements of game theory in the last one hundred years. They address past achievements, taking stock of what has been accomplished and contemplating potential future developments and challenges. Offering cross-disciplinary discussions between eminent researchers including five Nobel laureates, one Fields medalist and two Gödel prize winners, the contributors provide a fascinating landscape of game theory and its wide range of applications.

Page 1 of 2426