Learning With Submodular Functions

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Learning with Submodular Functions

Francis Bach,Now Publishers
Learning with Submodular Functions Book PDF

Author : Francis Bach,Now Publishers
Publisher : Unknown
Page : 228 pages
File Size : 52,5 Mb
Release : 2013
Category : Convex functions
ISBN : 1601987579

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Learning with Submodular Functions by Francis Bach,Now Publishers Pdf

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.

Learning with Submodular Functions

Francis Bach
Learning with Submodular Functions Book PDF

Author : Francis Bach
Publisher : Unknown
Page : 258 pages
File Size : 46,5 Mb
Release : 2013-11
Category : Computers
ISBN : 1601987560

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Learning with Submodular Functions by Francis Bach Pdf

Learning with Submodular Functions presents the theory of submodular functions in a self-contained way from a convex analysis perspective, presenting tight links between certain polyhedra, combinatorial optimization and convex optimization problems.

Submodular Functions and Optimization

Satoru Fujishige
Submodular Functions and Optimization Book PDF

Author : Satoru Fujishige
Publisher : Elsevier
Page : 411 pages
File Size : 46,8 Mb
Release : 2005-07-26
Category : Mathematics
ISBN : 9780080461625

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Submodular Functions and Optimization by Satoru Fujishige Pdf

It has widely been recognized that submodular functions play essential roles in efficiently solvable combinatorial optimization problems. Since the publication of the 1st edition of this book fifteen years ago, submodular functions have been showing further increasing importance in optimization, combinatorics, discrete mathematics, algorithmic computer science, and algorithmic economics, and there have been made remarkable developments of theory and algorithms in submodular functions. The 2nd edition of the book supplements the 1st edition with a lot of remarks and with new two chapters: "Submodular Function Minimization" and "Discrete Convex Analysis." The present 2nd edition is still a unique book on submodular functions, which is essential to students and researchers interested in combinatorial optimization, discrete mathematics, and discrete algorithms in the fields of mathematics, operations research, computer science, and economics. Self-contained exposition of the theory of submodular functions Selected up-to-date materials substantial to future developments Polyhedral description of Discrete Convex Analysis Full description of submodular function minimization algorithms Effective insertion of figures Useful in applied mathematics, operations research, computer science, and economics

Submodular Functions and Electrical Networks

H. Narayanan
Submodular Functions and Electrical Networks Book PDF

Author : H. Narayanan
Publisher : Elsevier
Page : 682 pages
File Size : 48,9 Mb
Release : 1997-05
Category : Computers
ISBN : 9780444825230

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Submodular Functions and Electrical Networks by H. Narayanan Pdf

There is a strong case for electrical network topologists and submodular function theorists being aware of each other's fields. Presenting a topological approach to electrical network theory, this book demonstrates the strong links that exist between submodular functions and electrical networks. The book contains: . a detailed discussion of graphs, matroids, vector spaces and the algebra of generalized minors, relevant to network analysis (particularly to the construction of efficient circuit simulators) . a detailed discussion of submodular function theory in its own right; topics covered include, various operations, dualization, convolution and Dilworth truncation as well as the related notions of prinicpal partition and principal lattice of partitions. In order to make the book useful to a wide audience, the material on electrical networks and that on submodular functions is presented independently of each other. The hybrid rank problem, the bridge between (topological) electrical network theory and submodular functions, is covered in the final chapter. The emphasis in the book is on low complexity algorithms, particularly based on bipartite graphs. The book is intended for self-study and is recommended to designers of VLSI algorithms. More than 300 problems, almost all of them with solutions, are included at the end of each chapter.

Probabilistic Machine Learning

Kevin P. Murphy
Probabilistic Machine Learning Book PDF

Author : Kevin P. Murphy
Publisher : MIT Press
Page : 1352 pages
File Size : 54,5 Mb
Release : 2023-08-15
Category : Computers
ISBN : 9780262376006

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Probabilistic Machine Learning by Kevin P. Murphy Pdf

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

Machine Learning and Knowledge Discovery in Databases

Peter A. Flach,Tijl De Bie,Nello Cristianini
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Peter A. Flach,Tijl De Bie,Nello Cristianini
Publisher : Springer
Page : 867 pages
File Size : 48,7 Mb
Release : 2012-09-11
Category : Computers
ISBN : 9783642334863

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Machine Learning and Knowledge Discovery in Databases by Peter A. Flach,Tijl De Bie,Nello Cristianini Pdf

This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.

Mathematical Programming The State of the Art

A. Bachem,M. Grötschel,B. Korte
Mathematical Programming The State of the Art Book PDF

Author : A. Bachem,M. Grötschel,B. Korte
Publisher : Springer Science & Business Media
Page : 662 pages
File Size : 49,7 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9783642688744

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Mathematical Programming The State of the Art by A. Bachem,M. Grötschel,B. Korte Pdf

In the late forties, Mathematical Programming became a scientific discipline in its own right. Since then it has experienced a tremendous growth. Beginning with economic and military applications, it is now among the most important fields of applied mathematics with extensive use in engineering, natural sciences, economics, and biological sciences. The lively activity in this area is demonstrated by the fact that as early as 1949 the first "Symposium on Mathe matical Programming" took place in Chicago. Since then mathematical programmers from all over the world have gath ered at the intfrnational symposia of the Mathematical Programming Society roughly every three years to present their recent research, to exchange ideas with their colleagues and to learn about the latest developments in their own and related fields. In 1982, the XI. International Symposium on Mathematical Programming was held at the University of Bonn, W. Germany, from August 23 to 27. It was organized by the Institut fUr Okonometrie und Operations Re search of the University of Bonn in collaboration with the Sonderforschungs bereich 21 of the Deutsche Forschungsgemeinschaft. This volume constitutes part of the outgrowth of this symposium and docu ments its scientific activities. Part I of the book contains information about the symposium, welcoming addresses, lists of committees and sponsors and a brief review about the Ful kerson Prize and the Dantzig Prize which were awarded during the opening ceremony.

Machine Learning and Knowledge Discovery in Databases

Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera
Publisher : Springer Nature
Page : 783 pages
File Size : 54,5 Mb
Release : 2021-02-24
Category : Computers
ISBN : 9783030676643

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Machine Learning and Knowledge Discovery in Databases by Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera Pdf

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.

Combinatorial Optimization -- Eureka, You Shrink!

Michael Jünger,Gerhard Reinelt,Giovanni Rinaldi
Combinatorial Optimization Eureka You Shrink Book PDF

Author : Michael Jünger,Gerhard Reinelt,Giovanni Rinaldi
Publisher : Springer
Page : 214 pages
File Size : 47,5 Mb
Release : 2003-07-01
Category : Mathematics
ISBN : 9783540364788

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Combinatorial Optimization -- Eureka, You Shrink! by Michael Jünger,Gerhard Reinelt,Giovanni Rinaldi Pdf

This book is dedicated to Jack Edmonds in appreciation of his ground breaking work that laid the foundations for a broad variety of subsequent results achieved in combinatorial optimization.The main part consists of 13 revised full papers on current topics in combinatorial optimization, presented at Aussois 2001, the Fifth Aussois Workshop on Combinatorial Optimization, March 5-9, 2001, and dedicated to Jack Edmonds.Additional highlights in this book are an account of an Aussois 2001 special session dedicated to Jack Edmonds including a speech given by William R. Pulleyblank as well as newly typeset versions of three up-to-now hardly accessible classical papers:- Submodular Functions, Matroids, and Certain Polyhedranbsp;nbsp; by Jack Edmonds- Matching: A Well-Solved Class of Integer Linear Programsnbsp;nbsp; by Jack Edmonds and Ellis L. Johnson- Theoretical Improvements in Algorithmic Efficiency for Network Flow Problemsnbsp;nbsp; by Jack Edmonds and Richard M. Karp.

IoT for Defense and National Security

Robert Douglass,Keith Gremban,Ananthram Swami,Stephan Gerali
IoT for Defense and National Security Book PDF

Author : Robert Douglass,Keith Gremban,Ananthram Swami,Stephan Gerali
Publisher : John Wiley & Sons
Page : 532 pages
File Size : 52,7 Mb
Release : 2023-01-19
Category : Political Science
ISBN : 9781119892144

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IoT for Defense and National Security by Robert Douglass,Keith Gremban,Ananthram Swami,Stephan Gerali Pdf

IoT for Defense and National Security Practical case-based guide illustrating the challenges and solutions of adopting IoT in both secure and hostile environments IoT for Defense and National Security covers topics on IoT security, architecture, robotics, sensing, policy, operations, and more, including the latest results from the premier IoT research initiative of the U.S. Defense Department, the Internet of Battle Things. The text also discusses challenges in converting defense industrial operations to IoT and summarizes policy recommendations for regulating government use of IoT in free societies. As a modern reference, this book covers multiple technologies in IoT including survivable tactical IoT using content-based routing, mobile ad-hoc networks, and electronically formed beams. Examples of IoT architectures include using KepServerEX for edge connectivity and AWS IoT Core and Amazon S3 for IoT data. To aid in reader comprehension, the text uses case studies illustrating the challenges and solutions for using robotic devices in defense applications, plus case studies on using IoT for a defense industrial base. Written by leading researchers and practitioners of IoT technology for defense and national security, IoT for Defense and National Security also includes information on: Changes in warfare driven by IoT weapons, logistics, and systems IoT resource allocation (monitoring existing resources and reallocating them in response to adversarial actions) Principles of AI-enabled processing for Internet of Battlefield Things, including machine learning and inference Vulnerabilities in tactical IoT communications, networks, servers and architectures, and strategies for securing them Adapting rapidly expanding commercial IoT to power IoT for defense For application engineers from defense-related companies as well as managers, policy makers, and academics, IoT for Defense and National Security is a one-of-a-kind resource, providing expansive coverage of an important yet sensitive topic that is often shielded from the public due to classified or restricted distributions.

Evolutionary Learning: Advances in Theories and Algorithms

Zhi-Hua Zhou,Yang Yu,Chao Qian
Evolutionary Learning Advances in Theories and Algorithms Book PDF

Author : Zhi-Hua Zhou,Yang Yu,Chao Qian
Publisher : Springer
Page : 361 pages
File Size : 49,5 Mb
Release : 2019-05-22
Category : Computers
ISBN : 9789811359569

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Evolutionary Learning: Advances in Theories and Algorithms by Zhi-Hua Zhou,Yang Yu,Chao Qian Pdf

Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.

Computer Vision – ECCV 2022

Shai Avidan,Gabriel Brostow,Moustapha Cissé,Giovanni Maria Farinella,Tal Hassner
Computer Vision ECCV 2022 Book PDF

Author : Shai Avidan,Gabriel Brostow,Moustapha Cissé,Giovanni Maria Farinella,Tal Hassner
Publisher : Springer Nature
Page : 819 pages
File Size : 50,5 Mb
Release : 2022-10-22
Category : Computers
ISBN : 9783031198397

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Computer Vision – ECCV 2022 by Shai Avidan,Gabriel Brostow,Moustapha Cissé,Giovanni Maria Farinella,Tal Hassner Pdf

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.

Internet and Network Economics

Christos Papadimitriou,Shuzhong Zhang
Internet and Network Economics Book PDF

Author : Christos Papadimitriou,Shuzhong Zhang
Publisher : Springer
Page : 734 pages
File Size : 51,7 Mb
Release : 2008-12-11
Category : Computers
ISBN : 9783540921851

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Internet and Network Economics by Christos Papadimitriou,Shuzhong Zhang Pdf

This book constitutes the refereed proceedings of the 4th International Workshop on Internet and Network Economics, WINE 2008, held in Shanghai, China, in December 2008. The 68 revised full papers presented together with 10 invited talks were carefully reviewed and selected from 126 submissions. The papers are organized in topical sections on market equilibrium, congestion games, information markets, nash equilibrium, network games, solution concepts, algorithms and optimization, mechanism design, equilibrium, online advertisement, sponsored search auctions, and voting problems.

Submodularity in Dynamics and Control of Networked Systems

Andrew Clark,Basel Alomair,Linda Bushnell,Radha Poovendran
Submodularity in Dynamics and Control of Networked Systems Book PDF

Author : Andrew Clark,Basel Alomair,Linda Bushnell,Radha Poovendran
Publisher : Springer
Page : 210 pages
File Size : 47,8 Mb
Release : 2015-12-21
Category : Technology & Engineering
ISBN : 9783319269771

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Submodularity in Dynamics and Control of Networked Systems by Andrew Clark,Basel Alomair,Linda Bushnell,Radha Poovendran Pdf

This book presents a framework for the control of networked systems utilizing submodular optimization techniques. The main focus is on selecting input nodes for the control of networked systems, an inherently discrete optimization problem with applications in power system stability, social influence dynamics, and the control of vehicle formations. The first part of the book is devoted to background information on submodular functions, matroids, and submodular optimization, and presents algorithms for distributed submodular optimization that are scalable to large networked systems. In turn, the second part develops a unifying submodular optimization approach to controlling networked systems based on multiple performance and controllability criteria. Techniques are introduced for selecting input nodes to ensure smooth convergence, synchronization, and robustness to environmental and adversarial noise. Submodular optimization is the first unifying approach towards guaranteeing both performance and controllability with provable optimality bounds in static as well as time-varying networks. Throughout the text, the submodular framework is illustrated with the help of numerical examples and application-based case studies in biological, energy and vehicular systems. The book effectively combines two areas of growing interest, and will be especially useful for researchers in control theory, applied mathematics, networking or machine learning with experience in submodular optimization but who are less familiar with the problems and tools available for networked systems (or vice versa). It will also benefit graduate students, offering consistent terminology and notation that greatly reduces the initial effort associated with beginning a course of study in a new area.

Integer Programming and Combinatorial Optimization

Jon Lee,Jens Vygen
Integer Programming and Combinatorial Optimization Book PDF

Author : Jon Lee,Jens Vygen
Publisher : Springer
Page : 418 pages
File Size : 40,6 Mb
Release : 2014-05-17
Category : Computers
ISBN : 9783319075570

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Integer Programming and Combinatorial Optimization by Jon Lee,Jens Vygen Pdf

This book constitutes the refereed proceedings of the 17th International Conference on Integer Programming and Combinatorial Optimization, IPCO 2014, held in Bonn, Germany, in June 2014. The 34 full papers presented were carefully reviewed and selected from 143 submissions. The conference is a forum for researchers and practitioners working on various aspects of integer programming and combinatorial optimization. The aim is to present recent developments in theory, computation, and applications in these areas. The scope of IPCO is viewed in a broad sense, to include algorithmic and structural results in integer programming and combinatorial optimization as well as revealing computational studies and novel applications of discrete optimization to practical problems.