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Active Learning And Submodular Functions
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Book Synopsis Active Learning and Submodular Functions by : Andrew Guillory
Download or read book Active Learning and Submodular Functions written by Andrew Guillory and published by . This book was released on 2012 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Active learning is a machine learning setting where the learning algorithm decides what data is labeled. Submodular functions are a class of set functions for which many optimization problems have efficient exact or approximate algorithms. We examine their connections. 1. We propose a new class of interactive submodular optimization problems which connect and generalize submodular optimization and active learning over a finite query set. We derive greedy algorithms with approximately optimal worst-case cost. These analyses apply to exact learning, approximate learning, learning in the presence of adversarial noise, and applications that mix learning and covering. 2. We consider active learning in a batch, transductive setting where the learning algorithm selects a set of examples to be labeled at once. In this setting we derive new error bounds which use symmetric submodular functions for regularization, and we give algorithms which approximately minimize these bounds. 3. We consider a repeated active learning setting where the learning algorithm solves a sequence of related learning problems. We propose an approach to this problem based on a new online prediction version of submodular set cover. A common theme in these results is the use of tools from submodular optimization to extend the breadth and depth of learning theory with an emphasis on non-stochastic settings.
Book Synopsis A Submodular Optimization Framework for Never-ending Learning by : Wael Emara
Download or read book A Submodular Optimization Framework for Never-ending Learning written by Wael Emara and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The revolution in information technology and the explosion in the use of computing devices in people's everyday activities has forever changed the perspective of the data mining and machine learning fields. The enormous amounts of easily accessible, information rich data is pushing the data analysis community in general towards a shift of paradigm. In the new paradigm, data comes in the form a stream of billions of records received everyday. The dynamic nature of the data and its sheer size makes it impossible to use the traditional notion of offline learning where the whole data is accessible at any time point. Moreover, no amount of human resources is enough to get expert feedback on the data. In this work we have developed a unified optimization based learning framework that approaches many of the challenges mentioned earlier. Specifically, we developed a Never-Ending Learning framework which combines incremental/online, semi-supervised, and active learning under a unified optimization framework. The established framework is based on the class of submodular optimization methods. At the core of this work we provide a novel formulation of the Semi-Supervised Support Vector Machines (S3VM) in terms of submodular set functions. The new formulation overcomes the non-convexity issues of the S3VM and provides a state of the art solution that is orders of magnitude faster than the cutting edge algorithms in the literature. Next, we provide a stream summarization technique via exemplar selection. This technique makes it possible to keep a fixed size exemplar representation of a data stream that can be used by any label propagation based semi-supervised learning technique. The compact data steam representation allows a wide range of algorithms to be extended to incremental/online learning scenario. Under the same optimization framework, we provide an active learning algorithm that constitute the feedback between the learning machine and an oracle. Finally, the developed Never-Ending Learning framework is essentially transductive in nature. Therefore, our last contribution is an inductive incremental learning technique for incremental training of SVM using the properties of local kernels. We demonstrated through this work the importance and wide applicability of the proposed methodologies.
Download or read book Active Learning written by Burr Chen and published by Springer Nature. This book was released on 2022-05-31 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks." We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations
Download or read book ECAI 2023 written by K. Gal and published by IOS Press. This book was released on 2023-10-18 with total page 3328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field.
Book Synopsis Computer Vision – ECCV 2022 by : Shai Avidan
Download or read book Computer Vision – ECCV 2022 written by Shai Avidan and published by Springer Nature. This book was released on 2022-10-22 with total page 819 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Book Synopsis Document Analysis and Recognition - ICDAR 2023 by : Gernot A. Fink
Download or read book Document Analysis and Recognition - ICDAR 2023 written by Gernot A. Fink and published by Springer Nature. This book was released on 2023-08-18 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: This six-volume set of LNCS 14187, 14188, 14189, 14190, 14191 and 14192 constitutes the refereed proceedings of the 17th International Conference on Document Analysis and Recognition, ICDAR 2021, held in San José, CA, USA, in August 2023. The 53 full papers were carefully reviewed and selected from 316 submissions, and are presented with 101 poster presentations. The papers are organized into the following topical sections: Graphics Recognition, Frontiers in Handwriting Recognition, Document Analysis and Recognition.
Book Synopsis Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014 by : Polina Golland
Download or read book Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014 written by Polina Golland and published by Springer. This book was released on 2014-08-31 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 8673, 8674, and 8675 constitutes the refereed proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, held in Boston, MA, USA, in September 2014. Based on rigorous peer reviews, the program committee carefully selected 253 revised papers from 862 submissions for presentation in three volumes. The 53 papers included in the third volume have been organized in the following topical sections: shape and population analysis; brain; diffusion MRI; and machine learning.
Book Synopsis Algorithmic Learning Theory by : Ronald Ortner
Download or read book Algorithmic Learning Theory written by Ronald Ortner and published by Springer. This book was released on 2016-10-12 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari, Italy, in October 2016, co-located with the 19th International Conference on Discovery Science, DS 2016. The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks. The papers are organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive learning; complexity of teaching models; inductive inference; online learning; bandits and reinforcement learning; and clustering.
Book Synopsis Combinatorial Optimization and Applications by : Yingshu Li
Download or read book Combinatorial Optimization and Applications written by Yingshu Li and published by Springer Nature. This book was released on 2019-12-06 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the proceedings of the 13th International Conference on Combinatorial Optimization and Applications, COCOA 2019, held in Xiamen, China, in December 2019. The 49 full papers presented in this volume were carefully reviewed and selected from 108 submissions. The papers cover the various topics, including cognitive radio networks, wireless sensor networks, cyber-physical systems, distributed and localized algorithm design and analysis, information and coding theory for wireless networks, localization, mobile cloud computing, topology control and coverage, security and privacy, underwater and underground networks, vehicular networks, information processing and data management, programmable service interfaces, energy-efficient algorithms, system and protocol design, operating system and middleware support, and experimental test-beds, models and case studies.
Book Synopsis Probabilistic Machine Learning by : Kevin P. Murphy
Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2023-08-15 with total page 1352 pages. Available in PDF, EPUB and Kindle. Book excerpt: An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty. An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning. Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributions Explores how to use probabilistic models and inference for causal inference and decision making Features online Python code accompaniment
Book Synopsis Learning with Submodular Functions by : Francis Bach
Download or read book Learning with Submodular Functions written by Francis Bach and published by . This book was released on 2013 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Submodular functions are relevant to machine learning for at least two reasons: (1) some problems may be expressed directly as the optimization of submodular functions and (2) the Lovász extension of submodular functions provides a useful set of regularization functions for supervised and unsupervised learning. In this monograph, we present the theory of submodular functions from a convex analysis perspective, presenting tight links between certain polyhedra, combinatorial optimization and convex optimization problems. In particular, we show how submodular function minimization is equivalent to solving a wide variety of convex optimization problems. This allows the derivation of new efficient algorithms for approximate and exact submodular function minimization with theoretical guarantees and good practical performance. By listing many examples of submodular functions, we review various applications to machine learning, such as clustering, experimental design, sensor placement, graphical model structure learning or subset selection, as well as a family of structured sparsity-inducing norms that can be derived and used from submodular functions.
Book Synopsis Combinatorial Optimization and Applications by : Weili Wu
Download or read book Combinatorial Optimization and Applications written by Weili Wu and published by Springer Nature. This book was released on 2020-12-04 with total page 834 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the proceedings of the 14th International Conference on Combinatorial Optimization and Applications, COCOA 2020, held in Dallas, TX, USA, in December 2020. The 55 full papers presented in this volume were carefully reviewed and selected from 104 submissions. The papers are grouped into the following topics: Approximation Algorithms; Scheduling; Network Optimization; Complexity and Logic; Search, Facility and Graphs; Geometric Problem; Sensors, Vehicles and Graphs; and Graph Problems. Due to the Corona pandemic this event was held virtually.
Book Synopsis Modelling, Computation and Optimization in Information Systems and Management Sciences by : Hoai An Le Thi
Download or read book Modelling, Computation and Optimization in Information Systems and Management Sciences written by Hoai An Le Thi and published by Springer Nature. This book was released on 2021-12-07 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings consist of 34 papers which have been submitted to the 4th international conference on Modelling, Computation & Optimization in Information Systems and Management Science (MCO 2021) held on 11-13 December, 2021 at Hanoi, Vietnam. The book is composed of 3 parts: Optimization of complex systems - models and methods, Machine Learning - algorithms and applications, and Cryptography. All chapters in the books discuss theoretical and algorithmic as well as practical issues connected with modelling, computation & optimization in Information Systems and Management Science. Researchers and practitioners in related areas will find a wealth of inspiring ideas and useful tools & techniques for their own work.
Download or read book Tractability written by Lucas Bordeaux and published by Cambridge University Press. This book was released on 2014-02-06 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the techniques developed to circumvent computational intractability, a key challenge in many areas of computer science.
Book Synopsis Combinatorial Algorithms by : Sun-Yuan Hsieh
Download or read book Combinatorial Algorithms written by Sun-Yuan Hsieh and published by Springer Nature. This book was released on 2023-06-02 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 34th International Workshop on Combinatorial Algorithms, IWOCA 2023, held in Tainan, Taiwan, during June 7–10, 2023. The 33 full papers included in this book were carefully reviewed and selected from 86 submissions. They were organized in topical sections as follows: algorithms and data structures; algorithmic and combinatorical aspects of cryptography and information security; algorithmic game theory and complexity of games; approximation algorithms; complexity theory; combinatorics and graph theory; combinatorial generation, enumeration and counting; combinatorial optimization; combinatorics of words; computational biology; computational geometry; decompositions and combinatorial designs; distributed and network algorithms; experimental combinatorics; fine-grained complexity; graph algorithms and modelling with graphs; graph drawing and graph labelling; network theory and temporal graphs; quantum computing and algorithms for quantum computers; online algorithms; parameterized and exact algorithms; probabilistic and randomized algorithms; and streaming algorithms.
Book Synopsis Advanced Methods and Deep Learning in Computer Vision by : E. R. Davies
Download or read book Advanced Methods and Deep Learning in Computer Vision written by E. R. Davies and published by Academic Press. This book was released on 2021-11-09 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students. Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field Illustrates principles with modern, real-world applications Suitable for self-learning or as a text for graduate courses
Book Synopsis Algorithmic Aspects in Information and Management by : Weili Wu
Download or read book Algorithmic Aspects in Information and Management written by Weili Wu and published by Springer Nature. This book was released on 2021-12-16 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 15th International Conference on Algorithmic Aspects in Information and Management, AAIM 2021, which was held online during December 20-22, 2021. The conference was originally planned to take place in Dallas, Texas, USA, but changed to a virtual event due to the COVID-19 pandemic. The 38 regular papers included in this book were carefully reviewed and selected from 62 submissions. They were organized in the following topical sections: approximation algorithms; scheduling; nonlinear combinatorial optimization; network problems; blockchain, logic, complexity and reliability; and miscellaneous.
