Conformal Prediction for Reliable Machine Learning

Conformal Prediction for Reliable Machine Learning
Author: Vineeth Balasubramanian,Shen-Shyang Ho,Vladimir Vovk
Publsiher: Newnes
Total Pages: 334
Release: 2014-04-23
ISBN 10: 0124017150
ISBN 13: 9780124017153
Language: EN, FR, DE, ES & NL

Conformal Prediction for Reliable Machine Learning Book Review:

The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection

Algorithmic Learning in a Random World

Algorithmic Learning in a Random World
Author: Vladimir Vovk,Alexander Gammerman,Glenn Shafer
Publsiher: Springer Science & Business Media
Total Pages: 324
Release: 2005-03-22
ISBN 10: 9780387001524
ISBN 13: 0387001522
Language: EN, FR, DE, ES & NL

Algorithmic Learning in a Random World Book Review:

Algorithmic 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.

Conformal and Probabilistic Prediction with Applications

Conformal and Probabilistic Prediction with Applications
Author: Alexander Gammerman,Zhiyuan Luo,Jesús Vega,Vladimir Vovk
Publsiher: Springer
Total Pages: 229
Release: 2016-04-16
ISBN 10: 331933395X
ISBN 13: 9783319333953
Language: EN, FR, DE, ES & NL

Conformal and Probabilistic Prediction with Applications Book Review:

This book constitutes the refereed proceedings of the 5th International Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2016, held in Madrid, Spain, in April 2016. The 14 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 23 submissions and cover topics on theory of conformal prediction; applications of conformal prediction; and machine learning.

Information Processing and Management of Uncertainty in Knowledge Based Systems

Information Processing and Management of Uncertainty in Knowledge Based Systems
Author: Marie-Jeanne Lesot,Susana Vieira,Marek Z. Reformat,João Paulo Carvalho,Anna Wilbik,Bernadette Bouchon-Meunier,Ronald R. Yager
Publsiher: Springer Nature
Total Pages: 753
Release: 2020-06-05
ISBN 10: 3030501469
ISBN 13: 9783030501464
Language: EN, FR, DE, ES & NL

Information Processing and Management of Uncertainty in Knowledge Based Systems Book Review:

This three volume set (CCIS 1237-1239) constitutes the proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020. The conference was scheduled to take place in Lisbon, Portugal, at University of Lisbon, but due to COVID-19 pandemic it was held virtually. The 173 papers were carefully reviewed and selected from 213 submissions. The papers are organized in topical sections: homage to Enrique Ruspini; invited talks; foundations and mathematics; decision making, preferences and votes; optimization and uncertainty; games; real world applications; knowledge processing and creation; machine learning I; machine learning II; XAI; image processing; temporal data processing; text analysis and processing; fuzzy interval analysis; theoretical and applied aspects of imprecise probabilities; similarities in artificial intelligence; belief function theory and its applications; aggregation: theory and practice; aggregation: pre-aggregation functions and other generalizations of monotonicity; aggregation: aggregation of different data structures; fuzzy methods in data mining and knowledge discovery; computational intelligence for logistics and transportation problems; fuzzy implication functions; soft methods in statistics and data analysis; image understanding and explainable AI; fuzzy and generalized quantifier theory; mathematical methods towards dealing with uncertainty in applied sciences; statistical image processing and analysis, with applications in neuroimaging; interval uncertainty; discrete models and computational intelligence; current techniques to model, process and describe time series; mathematical fuzzy logic and graded reasoning models; formal concept analysis, rough sets, general operators and related topics; computational intelligence methods in information modelling, representation and processing.

Statistical Learning and Data Sciences

Statistical Learning and Data Sciences
Author: Alexander Gammerman,Vladimir Vovk,Harris Papadopoulos
Publsiher: Springer
Total Pages: 444
Release: 2015-04-02
ISBN 10: 3319170910
ISBN 13: 9783319170916
Language: EN, FR, DE, ES & NL

Statistical Learning and Data Sciences Book Review:

This book constitutes the refereed proceedings of the Third International Symposium on Statistical Learning and Data Sciences, SLDS 2015, held in Egham, Surrey, UK, April 2015. The 36 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 59 submissions. The papers are organized in topical sections on statistical learning and its applications, conformal prediction and its applications, new frontiers in data analysis for nuclear fusion, and geometric data analysis.

Artificial Intelligence Applications and Innovations

Artificial Intelligence Applications and Innovations
Author: Lazaros Iliadis,Ilias Maglogiannis,Harris Papadopoulos,Spyros Sioutas,Christos Makris
Publsiher: Springer
Total Pages: 352
Release: 2014-09-15
ISBN 10: 3662447223
ISBN 13: 9783662447222
Language: EN, FR, DE, ES & NL

Artificial Intelligence Applications and Innovations Book Review:

This book constitutes the refereed proceedings of four AIAI 2014 workshops, co-located with the 10th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2014, held in Rhodes, Greece, in September 2014: the Third Workshop on Intelligent Innovative Ways for Video-to-Video Communications in Modern Smart Cities, IIVC 2014; the Third Workshop on Mining Humanistic Data, MHDW 2014; the Third Workshop on Conformal Prediction and Its Applications, CoPA 2014; and the First Workshop on New Methods and Tools for Big Data, MT4BD 2014. The 36 revised full papers presented were carefully reviewed and selected from numerous submissions. They cover a large range of topics in basic AI research approaches and applications in real world scenarios.

Advances and Trends in Artificial Intelligence From Theory to Practice

Advances and Trends in Artificial Intelligence  From Theory to Practice
Author: Franz Wotawa,Gerhard Friedrich,Ingo Pill,Roxane Koitz-Hristov,Moonis Ali
Publsiher: Springer
Total Pages: 865
Release: 2019-06-28
ISBN 10: 3030229998
ISBN 13: 9783030229993
Language: EN, FR, DE, ES & NL

Advances and Trends in Artificial Intelligence From Theory to Practice Book Review:

This book constitutes the thoroughly refereed proceedings of the 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, held in Graz, Austria, in July 2019. The 41 full papers and 32 short papers presented were carefully reviewed and selected from 151 submissions. The IEA/AIE 2019 conference will continue the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas. These areas include engineering, science, industry, automation and robotics, business and finance, medicine and biomedicine, bioinformatics, cyberspace, and human-machine interactions. IEA/AIE 2019 will have a special focus on automated driving and autonomous systems and also contributions dealing with such systems or their verification and validation as well.

Runtime Verification

Runtime Verification
Author: Bernd Finkbeiner,Leonardo Mariani
Publsiher: Springer Nature
Total Pages: 413
Release: 2019-10-03
ISBN 10: 3030320790
ISBN 13: 9783030320799
Language: EN, FR, DE, ES & NL

Runtime Verification Book Review:

This book constitutes the refereed proceedings of the 19th International Conference on Runtime Verification, RV 2019, held in Porto, Portugal, in October 2019. The 25 regular papers presented in this book were carefully reviewed and selected from 38 submissions. The RV conference is concerned with all aspects of monitoring and analysis of hardware, software and more general system executions. Runtime verification techniques are lightweight techniques to assess system correctness, reliability, and robustness; these techniques are significantly more powerful and versatile than conventional testing, and more practical than exhaustive formal verification. Chapter “Assumption-Based Runtime Verification with Partial Observability and Resets” and chapter “NuRV: a nuXmv Extension for Runtime Verification“ are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
Author: Toon Calders,Floriana Esposito,Eyke Hüllermeier,Rosa Meo
Publsiher: Springer
Total Pages: 715
Release: 2014-09-01
ISBN 10: 3662448513
ISBN 13: 9783662448519
Language: EN, FR, DE, ES & NL

Machine Learning and Knowledge Discovery in Databases Book Review:

This three-volume set LNAI 8724, 8725 and 8726 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2014, held in Nancy, France, in September 2014. The 115 revised research papers presented together with 13 demo track papers, 10 nectar track papers, 8 PhD track papers, and 9 invited talks were carefully reviewed and selected from 550 submissions. The papers cover the latest high-quality interdisciplinary research results in all areas related to machine learning and knowledge discovery in databases.

Dynamic Data Driven Applications Systems

Dynamic Data Driven Applications Systems
Author: Frederica Darema,Erik Blasch,Sai Ravela,Alex Aved
Publsiher: Springer Nature
Total Pages: 360
Release: 2020-11-02
ISBN 10: 3030617254
ISBN 13: 9783030617257
Language: EN, FR, DE, ES & NL

Dynamic Data Driven Applications Systems Book Review:

This book constitutes the refereed proceedings of the Third International Conference on Dynamic Data Driven Application Systems, DDDAS 2020, held in Boston, MA, USA, in October 2020. The 21 full papers and 14 short papers presented in this volume were carefully reviewed and selected from 40 submissions. They cover topics such as: digital twins; environment cognizant adaptive-planning systems; energy systems; materials systems; physics-based systems analysis; imaging methods and systems; and learning systems.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
Author: Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera
Publsiher: Springer Nature
Total Pages: 755
Release: 2021
ISBN 10: 3030676641
ISBN 13: 9783030676643
Language: EN, FR, DE, ES & NL

Machine Learning and Knowledge Discovery in Databases Book Review:

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track. .

Measures of Complexity

Measures of Complexity
Author: Vladimir Vovk,Harris Papadopoulos,Alexander Gammerman
Publsiher: Springer
Total Pages: 399
Release: 2015-09-03
ISBN 10: 3319218522
ISBN 13: 9783319218526
Language: EN, FR, DE, ES & NL

Measures of Complexity Book Review:

This book brings together historical notes, reviews of research developments, fresh ideas on how to make VC (Vapnik–Chervonenkis) guarantees tighter, and new technical contributions in the areas of machine learning, statistical inference, classification, algorithmic statistics, and pattern recognition. The contributors are leading scientists in domains such as statistics, mathematics, and theoretical computer science, and the book will be of interest to researchers and graduate students in these domains.

Scalable Uncertainty Management

Scalable Uncertainty Management
Author: Nahla Ben Amor,Benjamin Quost,Martin Theobald
Publsiher: Springer Nature
Total Pages: 452
Release: 2019-12-02
ISBN 10: 3030355144
ISBN 13: 9783030355142
Language: EN, FR, DE, ES & NL

Scalable Uncertainty Management Book Review:

This book constitutes the refereed proceedings of the 13th International Conference on Scalable Uncertainty Management, SUM 2019, which was held in Compiègne, France, in December 2019. The 25 full, 4 short, 4 tutorial, 2 invited keynote papers presented in this volume were carefully reviewed and selected from 44 submissions. The conference is dedicated to the management of large amounts of complex, uncertain, incomplete, or inconsistent information. New approaches have been developed on imprecise probabilities, fuzzy set theory, rough set theory, ordinal uncertainty representations, or even purely qualitative models.

Runtime Verification

Runtime Verification
Author: Lu Feng,Dana Fisman
Publsiher: Springer Nature
Total Pages: 331
Release: 2021-10-05
ISBN 10: 3030884945
ISBN 13: 9783030884949
Language: EN, FR, DE, ES & NL

Runtime Verification Book Review:

This book constitutes the refereed proceedings of the 21st International Conference on Runtime Verification, RV 2021, held virtually during October 11-14, 2021. The 11 regular papers and 7 short/tool/benchmark papers presented in this book were carefully reviewed and selected from 40 submissions. Also included is one tutorial paper. The RV conference is concerned with all aspects of monitoring and analysis of hardware, software and more general system executions.

Advances in Machine Learning Research and Application 2013 Edition

Advances in Machine Learning Research and Application  2013 Edition
Author: Anonim
Publsiher: ScholarlyEditions
Total Pages: 1078
Release: 2013-06-21
ISBN 10: 1481683195
ISBN 13: 9781481683197
Language: EN, FR, DE, ES & NL

Advances in Machine Learning Research and Application 2013 Edition Book Review:

Advances in Machine Learning Research and Application: 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Artificial Intelligence. The editors have built Advances in Machine Learning Research and Application: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Artificial Intelligence in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Advances in Machine Learning Research and Application: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Computational Intelligence in Data Mining Volume 3

Computational Intelligence in Data Mining   Volume 3
Author: Lakhmi C. Jain,Himansu Sekhar Behera,Jyotsna Kumar Mandal,Durga Prasad Mohapatra
Publsiher: Springer
Total Pages: 717
Release: 2014-12-11
ISBN 10: 8132222024
ISBN 13: 9788132222026
Language: EN, FR, DE, ES & NL

Computational Intelligence in Data Mining Volume 3 Book Review:

The contributed volume aims to explicate and address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. Data Mining aims at the automatic discovery of underlying non-trivial knowledge from datasets by applying intelligent analysis techniques. The interest in this research area has experienced a considerable growth in the last years due to two key factors: (a) knowledge hidden in organizations’ databases can be exploited to improve strategic and managerial decision-making; (b) the large volume of data managed by organizations makes it impossible to carry out a manual analysis. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.

Fuzzy Logic and Soft Computing Applications

Fuzzy Logic and Soft Computing Applications
Author: Alfredo Petrosino,Vincenzo Loia,Witold Pedrycz
Publsiher: Springer
Total Pages: 281
Release: 2017-02-06
ISBN 10: 3319529625
ISBN 13: 9783319529622
Language: EN, FR, DE, ES & NL

Fuzzy Logic and Soft Computing Applications Book Review:

This book constitutes the proceedings of the 11th International Workshop on Fuzzy Logic and Applications, WILF 2016, held in Naples, Italy, in December 2016. The 22 revised full papers presented together with 2 invited lectures were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on fuzzy measures and transforms; granularity and multi-logics, clustering and learning; knowledge systems; and soft computing and applications.

Characterizing the Limits and Defenses of Machine Learning in Adversarial Settings

Characterizing the Limits and Defenses of Machine Learning in Adversarial Settings
Author: Nicolas Papernot
Publsiher: Unknown
Total Pages: 135
Release: 2018
ISBN 10: 1928374650XXX
ISBN 13: OCLC:1038418985
Language: EN, FR, DE, ES & NL

Characterizing the Limits and Defenses of Machine Learning in Adversarial Settings Book Review:

Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as object recognition, autonomous systems, security diagnostics, and playing the game of Go. Machine learning is not only a new paradigm for building software and systems, it is bringing social disruption at scale. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical communitys understanding of the nature and extent of these vulnerabilities remains limited. In this thesis, I focus my study on the integrity of ML models. Integrity refers here to the faithfulness of model predictions with respect to an expected outcome. This property is at the core of traditional machine learning evaluation, as demonstrated by the pervasiveness of metrics such as accuracy among practitioners. A large fraction of ML techniques were designed for benign execution environments. Yet, the presence of adversaries may invalidate some of these underlying assumptions by forcing a mismatch between the distributions on which the model is trained and tested. As ML is increasingly applied and being relied on for decision-making in critical applications like transportation or energy, the models produced are becoming a target for adversaries who have a strong incentive to force ML to mispredict. I explore the space of attacks against ML integrity at test time. Given full or limited access to a trained model, I devise strategies that modify the test data to create a worst-case drift between the training and test distributions. The implications of this part of my research is that an adversary with very weak access to a system, and little knowledge about the ML techniques it deploys, can nevertheless mount powerful attacks against such systems as long as she has the capability of interacting with it as an oracle: i.e., send inputs of the adversarys choice and observe the ML prediction. This systematic exposition of the poor generalization of ML models indicates the lack of reliable confidence estimates when the model is making predictions far from its training data. Hence, my efforts to increase the robustness of models to these adversarial manipulations strive to decrease the confidence of predictions made far from the training distribution. Informed by my progress on attacks operating in the black-box threat model, I first identify limitations to two defenses: defensive distillation and adversarial training. I then describe recent defensive efforts addressing these shortcomings. To this end, I introduce the Deep k-Nearest Neighbors classifier, which augments deep neural networks with an integrity check at test time. The approach compares internal representations produced by the deep neural network on test data with the ones learned on its training points. Using the labels of training points whose representations neighbor the test input across the deep neural networks layers, I estimate the nonconformity of the prediction with respect to the models training data. An application of conformal prediction methodology then paves the way for more reliable estimates of the models prediction credibility, i.e., how well the prediction is supported by training data. In turn, we distinguish legitimate test data with high credibility from adversarial data with low credibility. This research calls for future efforts to investigate the robustness of individual layers of deep neural networks rather than treating the model as a black-box. This aligns well with the modular nature of deep neural networks, which orchestrate simple computations to model complex functions. This also allows us to draw connections to other areas like interpretability in ML, which seeks to answer the question of: How can we provide an explanation for the model prediction to a human? Another by-product of this research direction is that I better distinguish vulnerabilities of ML models that are a consequence of the ML algorithms from those that can be explained by artifacts in the data.

Introducing MLOps

Introducing MLOps
Author: Mark Treveil,Nicolas Omont,Clément Stenac,Kenji Lefevre,Du Phan,Joachim Zentici,Adrien Lavoillotte,Makoto Miyazaki,Lynn Heidmann
Publsiher: "O'Reilly Media, Inc."
Total Pages: 186
Release: 2020-11-30
ISBN 10: 1098116429
ISBN 13: 9781098116422
Language: EN, FR, DE, ES & NL

Introducing MLOps Book Review:

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized

Conformal Predictions in Multimedia Pattern Recognition

Conformal Predictions in Multimedia Pattern Recognition
Author: Vineeth Nallure Balasubramanian
Publsiher: Unknown
Total Pages: 249
Release: 2010
ISBN 10: 1928374650XXX
ISBN 13: OCLC:800208361
Language: EN, FR, DE, ES & NL

Conformal Predictions in Multimedia Pattern Recognition Book Review:

The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning techniques today are not yet fully equipped to be trusted with this critical task. This work seeks to address this fundamental knowledge gap. Existing approaches that provide a measure of confidence on a prediction such as learning algorithms based on the Bayesian theory or the Probably Approximately Correct theory require strong assumptions or often produce results that are not practical or reliable. The recently developed Conformal Predictions (CP) framework - which is based on the principles of hypothesis testing, transductive inference and algorithmic randomness - provides a game-theoretic approach to the estimation of confidence with several desirable properties such as online calibration and generalizability to all classification and regression methods.