Cause Effect Pairs In Machine Learning

Author : Isabelle Guyon,Alexander Statnikov,Berna Bakir Batu
Publisher : Springer Nature
Page : 372 pages
File Size : 47,7 Mb
Release : 2019-10-22
Category : Computers
ISBN : 9783030218102

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Cause Effect Pairs in Machine Learning by Isabelle Guyon,Alexander Statnikov,Berna Bakir Batu Pdf

This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website. Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.

Author : Petra Perner
Publisher : Springer
Page : 470 pages
File Size : 54,7 Mb
Release : 2018-07-09
Category : Computers
ISBN : 9783319961361

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Machine Learning and Data Mining in Pattern Recognition by Petra Perner Pdf

This two-volume set LNAI 10934 and LNAI 10935 constitutes the refereed proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018. The 92 regular papers presented in this two-volume set were carefully reviewed and selected from 298 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.

Author : Sheng Li,Zhixuan Chu
Publisher : Springer Nature
Page : 302 pages
File Size : 50,5 Mb
Release : 2023-11-25
Category : Technology & Engineering
ISBN : 9783031350511

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Machine Learning for Causal Inference by Sheng Li,Zhixuan Chu Pdf

This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields. Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.

Author : Hugo Jair Escalante,Sergio Escalera,Isabelle Guyon,Xavier Baró,Yağmur Güçlütürk,Umut Güçlü,Marcel van Gerven
Publisher : Springer
Page : 299 pages
File Size : 42,6 Mb
Release : 2018-11-29
Category : Computers
ISBN : 9783319981314

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Explainable and Interpretable Models in Computer Vision and Machine Learning by Hugo Jair Escalante,Sergio Escalera,Isabelle Guyon,Xavier Baró,Yağmur Güçlütürk,Umut Güçlü,Marcel van Gerven Pdf

This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations

Author : Jonas Peters,Dominik Janzing,Bernhard Scholkopf
Publisher : MIT Press
Page : 289 pages
File Size : 47,7 Mb
Release : 2017-11-29
Category : Computers
ISBN : 9780262037310

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Elements of Causal Inference by Jonas Peters,Dominik Janzing,Bernhard Scholkopf Pdf

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Author : Jordi Vallverdú
Publisher : Springer Nature
Page : 110 pages
File Size : 44,7 Mb
Release : 2024-07-03
Category : Electronic
ISBN : 9789819731879

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Causality for Artificial Intelligence by Jordi Vallverdú Pdf

Author : Lu Fang,Daniel Povey,Guangtao Zhai,Tao Mei,Ruiping Wang
Publisher : Springer Nature
Page : 660 pages
File Size : 47,8 Mb
Release : 2023-01-01
Category : Computers
ISBN : 9783031205002

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Artificial Intelligence by Lu Fang,Daniel Povey,Guangtao Zhai,Tao Mei,Ruiping Wang Pdf

This three-volume set LNCS 13604-13606 constitutes revised selected papers presented at the Second CAAI International Conference on Artificial Intelligence, held in Beijing, China, in August 2022. CICAI is a summit forum in the field of artificial intelligence and the 2022 forum was hosted by Chinese Association for Artificial Intelligence (CAAI). The 164 papers were thoroughly reviewed and selected from 521 submissions. CICAI aims to establish a global platform for international academic exchange, promote advanced research in AI and its affiliated disciplines such as machine learning, computer vision, natural language, processing, and data mining, amongst others.

Author : A. Dimou,S. Neumaier,T. Pellegrini
Publisher : IOS Press
Page : 236 pages
File Size : 53,7 Mb
Release : 2022-09-29
Category : Computers
ISBN : 9781643683218

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Towards a Knowledge-Aware AI by A. Dimou,S. Neumaier,T. Pellegrini Pdf

Semantic systems lie at the heart of modern computing, interlinking with areas as diverse as AI, data science, knowledge discovery and management, big data analytics, e-commerce, enterprise search, technical documentation, document management, business intelligence, enterprise vocabulary management, machine learning, logic programming, content engineering, social computing, and the Semantic Web. This book presents the proceedings of SEMANTiCS 2022, the 18th International Conference on Semantic Systems, held as a hybrid event – live in Vienna, Austria and online – from 12 to 15 September 2022. The SEMANTiCS conference is an annual meeting place for the professionals and researchers who make semantic computing work, who understand its benefits and encounter its limitations, and is attended by information managers, IT architects, software engineers, and researchers from organizations ranging from research facilities and NPOs, through public administrations to the largest companies in the world. The theme and subtitle of the 2022 conference was Towards A Knowledge-Aware AI, and the book contains 15 papers, selected on the basis of quality, impact and scientific merit following a rigorous review process which resulted in an acceptance rate of 29%. The book is divided into four chapters: semantics in data quality, standards and protection; representation learning and reasoning for downstream AI tasks; ontology development; and learning over complementary knowledge. Providing an overview of emerging trends and topics in the wide area of semantic computing, the book will be of interest to anyone involved in the development and deployment of computer technology and AI systems.

Author : Judea Pearl
Publisher : Createspace Independent Publishing Platform
Page : 0 pages
File Size : 48,9 Mb
Release : 2015
Category : Causation
ISBN : 1507894295

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An Introduction to Causal Inference by Judea Pearl Pdf

This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.

Author : Michele Berlingerio,Francesco Bonchi,Thomas Gärtner,Neil Hurley,Georgiana Ifrim
Publisher : Springer
Page : 866 pages
File Size : 41,9 Mb
Release : 2019-01-22
Category : Computers
ISBN : 9783030109288

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Machine Learning and Knowledge Discovery in Databases by Michele Berlingerio,Francesco Bonchi,Thomas Gärtner,Neil Hurley,Georgiana Ifrim Pdf

The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.

Author : Qingshan Liu,Hanzi Wang,Zhanyu Ma,Weishi Zheng,Hongbin Zha,Xilin Chen,Liang Wang,Rongrong Ji
Publisher : Springer Nature
Page : 525 pages
File Size : 41,9 Mb
Release : 2024-01-25
Category : Computers
ISBN : 9789819985401

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Pattern Recognition and Computer Vision by Qingshan Liu,Hanzi Wang,Zhanyu Ma,Weishi Zheng,Hongbin Zha,Xilin Chen,Liang Wang,Rongrong Ji Pdf

The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis.

Author : Luis A. Leiva,Cédric Pruski,Réka Markovich,Amro Najjar,Christoph Schommer
Publisher : Springer Nature
Page : 259 pages
File Size : 54,9 Mb
Release : 2022-01-11
Category : Computers
ISBN : 9783030938420

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Artificial Intelligence and Machine Learning by Luis A. Leiva,Cédric Pruski,Réka Markovich,Amro Najjar,Christoph Schommer Pdf

This book contains a selection of the best papers of the 33rd Benelux Conference on Artificial Intelligence, BNAIC/ BENELEARN 2021, held in Esch-sur-Alzette, Luxembourg, in November 2021. The 14 papers presented in this volume were carefully reviewed and selected from 46 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning, human-agent interaction, AI and education, and data analysis.

Author : Ernesto Damiani,George Spanoudakis,Leszek A. Maciaszek
Publisher : Springer Nature
Page : 413 pages
File Size : 50,7 Mb
Release : 2020-02-08
Category : Computers
ISBN : 9783030402235

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Evaluation of Novel Approaches to Software Engineering by Ernesto Damiani,George Spanoudakis,Leszek A. Maciaszek Pdf

This book constitutesselected, revised and extended papers of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2019, held in Heraklion, Crete, Greece, in May 2019. The 19 revised full papers presented were carefully reviewed and selected from 102 submissions. The papers included in this book contribute to the understanding of relevant trends of current research on novel approaches to software engineering for the development and maintenance of systems and applications, specically with relation to: model-driven software engineering, requirements engineering, empirical software engineering, service-oriented software engineering, business process management and engineering, knowledge management and engineering, reverse software engineering, software process improvement, software change and configuration management, software metrics, software patterns and refactoring, application integration, software architecture, cloud computing, and formal methods.

Author : Alexander Gelbukh,Félix Castro Espinoza,Sofía N. Galicia-Haro
Publisher : Springer
Page : 522 pages
File Size : 52,7 Mb
Release : 2014-11-05
Category : Computers
ISBN : 9783319136509

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Nature-Inspired Computation and Machine Learning by Alexander Gelbukh,Félix Castro Espinoza,Sofía N. Galicia-Haro Pdf

The two-volume set LNAI 8856 and LNAI 8857 constitutes the proceedings of the 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, held in Tuxtla, Mexico, in November 2014. The total of 87 papers plus 1 invited talk presented in these proceedings were carefully reviewed and selected from 348 submissions. The first volume deals with advances in human-inspired computing and its applications. It contains 44 papers structured into seven sections: natural language processing, natural language processing applications, opinion mining, sentiment analysis, and social network applications, computer vision, image processing, logic, reasoning, and multi-agent systems, and intelligent tutoring systems. The second volume deals with advances in nature-inspired computation and machine learning and contains also 44 papers structured into eight sections: genetic and evolutionary algorithms, neural networks, machine learning, machine learning applications to audio and text, data mining, fuzzy logic, robotics, planning, and scheduling, and biomedical applications.

Author : Hao-Ren Ke,Chei Sian Lee,Kazunari Sugiyama
Publisher : Springer Nature
Page : 483 pages
File Size : 48,7 Mb
Release : 2021-11-30
Category : Computers
ISBN : 9783030916695

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Towards Open and Trustworthy Digital Societies by Hao-Ren Ke,Chei Sian Lee,Kazunari Sugiyama Pdf

This book constitutes the refereed proceedings of the 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, which was held in December 2021. Due to COVID-19 pandemic the conference was held virtually. The 17 full, 14 short, and 5 practice papers presented in this volume were carefully reviewed and selected from 87 submissions. The papers were organized in topical sections named: Knowledge Discovery from Digital Collections; Search for Better User Experience; Information Extraction; Multimedia; Text Classification and Matching; Data Infrastructure for Digital Libraries; Data Modeling; Neural-based Learning.