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Relational Machine Learning Algorithms
Relational Machine Learning Algorithms by Alireza Samadianzakaria Bachelor of Science, Sharif University of Technology, 2016 Submitted to the Graduate Faculty of the Department of Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2021 UNIVERSITY OF PITTSBURGH DEPARTMENT OF COMPUTER SCIENCE This dissertation was presented by Alireza Samadianzakaria It was defended on July 7, 2021 and approved by Dr. Kirk Pruhs, Department of Computer Science, University of Pittsburgh Dr. Panos Chrysanthis, Department of Computer Science, University of Pittsburgh Dr. Adriana Kovashka, Department of Computer Science, University of Pittsburgh Dr. Benjamin Moseley, Tepper School of Business, Carnegie Mellon University ii Copyright c by Alireza Samadianzakaria 2021 iii Relational Machine Learning Algorithms Alireza Samadianzakaria, PhD University of Pittsburgh, 2021 The majority of learning tasks faced by data scientists involve relational data, yet most standard algorithms for standard learning problems are not designed to accept relational data as input. The standard practice to address this issue is to join the relational data to create the type of geometric input that standard learning algorithms expect. Unfortunately, this standard practice has exponential worst-case time and space complexity. This leads us to consider what we call the Relational Learning Question: \Which standard learning algorithms can be efficiently implemented on relational data, and for those that cannot, is there an alternative algorithm that can be efficiently implemented on relational data and that has similar performance guarantees to the standard algorithm?" In this dissertation, we address the relational learning question for the well-known prob- lems of support vector machine (SVM), logistic regression, and k-means clustering. -
Computer Science and Decision Theory Fred S. Roberts1 Abstract 1
Computer Science and Decision Theory Fred S. Roberts1 DIMACS Center, Rutgers University, Piscataway, NJ 08854 USA [email protected] Abstract This paper reviews applications in computer science that decision theorists have addressed for years, discusses the requirements posed by these applications that place great strain on decision theory/social science methods, and explores applications in the social and decision sciences of newer decision-theoretic methods developed with computer science applications in mind. The paper deals with the relation between computer science and decision-theoretic methods of consensus, with the relation between computer science and game theory and decisions, and with \algorithmic decision theory." 1 Introduction Many applications in computer science involve issues and problems that decision theorists have addressed for years, issues of preference, utility, conflict and cooperation, allocation, incentives, consensus, social choice, and measurement. A similar phenomenon is apparent more generally at the interface between computer sci- ence and the social sciences. We have begun to see the use of methods developed by decision theorists/social scientists in a variety of computer science applications. The requirements posed by these computer science applications place great strain on the decision theory/social science methods because of the sheer size of the problems addressed, new contexts in which computational power of agents becomes an issue, limitations on information possessed by players, and the sequential nature of repeated applications. Hence, there is a great need to develop a new generation of methods to satisfy these computer science requirements. In turn, these new methods will provide powerful new tools for social scientists in general and decision theorists in particular. -
Computational Learning Theory: New Models and Algorithms
Computational Learning Theory: New Models and Algorithms by Robert Hal Sloan S.M. EECS, Massachusetts Institute of Technology (1986) B.S. Mathematics, Yale University (1983) Submitted to the Department- of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 1989 @ Robert Hal Sloan, 1989. All rights reserved The author hereby grants to MIT permission to reproduce and to distribute copies of this thesis document in whole or in part. Signature of Author Department of Electrical Engineering and Computer Science May 23, 1989 Certified by Ronald L. Rivest Professor of Computer Science Thesis Supervisor Accepted by Arthur C. Smith Chairman, Departmental Committee on Graduate Students Abstract In the past several years, there has been a surge of interest in computational learning theory-the formal (as opposed to empirical) study of learning algorithms. One major cause for this interest was the model of probably approximately correct learning, or pac learning, introduced by Valiant in 1984. This thesis begins by presenting a new learning algorithm for a particular problem within that model: learning submodules of the free Z-module Zk. We prove that this algorithm achieves probable approximate correctness, and indeed, that it is within a log log factor of optimal in a related, but more stringent model of learning, on-line mistake bounded learning. We then proceed to examine the influence of noisy data on pac learning algorithms in general. Previously it has been shown that it is possible to tolerate large amounts of random classification noise, but only a very small amount of a very malicious sort of noise. -
On Computational Tractability for Rational Verification
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) On Computational Tractability for Rational Verification Julian Gutierrez1 , Muhammad Najib1 , Giuseppe Perelli2 , Michael Wooldridge1 1Department of Computer Science, University of Oxford, UK 2Department of Informatics, University of Leicester, UK fjulian.gutierrez, mnajib, [email protected], [email protected] Abstract theoretic (e.g., Nash) equilibrium. Unlike model checking, rational verification is still in its infancy: the main ideas, Rational verification involves checking which formal models, and reasoning techniques underlying rational temporal logic properties hold of a concur- verification are under development, while current tool sup- rent/multiagent system, under the assumption that port is limited and cannot yet handle systems of industrial agents in the system choose strategies in game the- size [Toumi et al., 2015; Gutierrez et al., 2018a]. oretic equilibrium. Rational verification can be un- derstood as a counterpart of model checking for One key difficulty is that rational verification is computa- multiagent systems, but while model checking can tionally much harder than model checking, because checking be done in polynomial time for some temporal logic equilibrium properties requires quantifying over the strategies specification languages such as CTL, and polyno- available to players in the system. Rational verification is also mial space with LTL specifications, rational ver- different from model checking in the kinds of properties that ification is much more intractable: 2EXPTIME- each technique tries to check: while model checking is inter- any complete with LTL specifications, even when using ested in correctness with respect to possible behaviour of explicit-state system representations. -
Downloaded from 128.205.114.91 on Sun, 19 May 2013 20:14:53 PM All Use Subject to JSTOR Terms and Conditions 660 REVIEWS
Association for Symbolic Logic http://www.jstor.org/stable/2274542 . Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp . JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. Association for Symbolic Logic is collaborating with JSTOR to digitize, preserve and extend access to The Journal of Symbolic Logic. http://www.jstor.org This content downloaded from 128.205.114.91 on Sun, 19 May 2013 20:14:53 PM All use subject to JSTOR Terms and Conditions 660 REVIEWS The penultimate chapter, Real machines, is the major exposition on Al techniques and programs found in this book. It is here that heuristic search is discussed and classic programs such as SHRDLU and GPS are described. It is here that a sampling of Al material and its flavor as research is presented. Some of the material here is repeated without real analysis. For example, the author repeats the standard textbook mistake on the size of the chess space. On page 178, he states that 10120 is the size of this space, and uses this to suggest that no computer will ever play perfect chess. Actually, an estimate of 1040 is more realistic. If one considers that no chess board can have more than sixteen pieces of each color and there are many configurations that are illegal or equivalent, then the state space is reduced considerably. -
Research Notices
AMERICAN MATHEMATICAL SOCIETY Research in Collegiate Mathematics Education. V Annie Selden, Tennessee Technological University, Cookeville, Ed Dubinsky, Kent State University, OH, Guershon Hare I, University of California San Diego, La jolla, and Fernando Hitt, C/NVESTAV, Mexico, Editors This volume presents state-of-the-art research on understanding, teaching, and learning mathematics at the post-secondary level. The articles are peer-reviewed for two major features: (I) advancing our understanding of collegiate mathematics education, and (2) readability by a wide audience of practicing mathematicians interested in issues affecting their students. This is not a collection of scholarly arcana, but a compilation of useful and informative research regarding how students think about and learn mathematics. This series is published in cooperation with the Mathematical Association of America. CBMS Issues in Mathematics Education, Volume 12; 2003; 206 pages; Softcover; ISBN 0-8218-3302-2; List $49;AII individuals $39; Order code CBMATH/12N044 MATHEMATICS EDUCATION Also of interest .. RESEARCH: AGul<lelbrthe Mathematics Education Research: Hothomatldan- A Guide for the Research Mathematician --lllll'tj.M...,.a.,-- Curtis McKnight, Andy Magid, and -- Teri J. Murphy, University of Oklahoma, Norman, and Michelynn McKnight, Norman, OK 2000; I 06 pages; Softcover; ISBN 0-8218-20 16-8; List $20;AII AMS members $16; Order code MERN044 Teaching Mathematics in Colleges and Universities: Case Studies for Today's Classroom Graduate Student Edition Faculty -
An Efficient Boosting Algorithm for Combining Preferences Raj
An Efficient Boosting Algorithm for Combining Preferences by Raj Dharmarajan Iyer Jr. Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 1999 © Massachusetts Institute of Technology 1999. All rights reserved. A uth or .............. ..... ..... .................................. Department of E'ectrical ngineering and Computer Science August 24, 1999 C ertified by .. ................. ...... .. .............. David R. Karger Associate Professor Thesis Supervisor Accepted by............... ........ Arthur C. Smith Chairman, Departmental Committe Graduate Students MACHU OF TEC Lo NOV LIBRARIES An Efficient Boosting Algorithm for Combining Preferences by Raj Dharmarajan Iyer Jr. Submitted to the Department of Electrical Engineering and Computer Science on August 24, 1999, in partial fulfillment of the requirements for the degree of Master of Science Abstract The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting algorithm for combining preferences called RankBoost. We also describe an efficient implementation of the algorithm for certain natural cases. We discuss two experiments we carried out to assess the performance of RankBoost. In the first experi- ment, we used the algorithm to combine different WWW search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of Rank- Boost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. -
Logics for Concurrency
Lecture Notes in Computer Science 1043 Edited by G. Goos, J. Hartmanis and J. van Leeuwen Advisory Board: W. Brauer D. Gries J. Stoer Faron Moiler Graham Birtwistle (Eds.) Logics for Concurrency Structure versus Automata ~ Springer Series Editors Gerhard Goos, Karlsruhe University, Germany Juris Hartmanis, Cornetl University, NY, USA Jan van Leeuwen, Utrecht University, The Netherlands Volume Editors Faron Moller Department ofTeleinformatics, Kungl Tekniska H6gskolan Electrum 204, S-164 40 Kista, Sweden Graham Birtwistle School of Computer Studies, University of Leeds Woodhouse Road, Leeds LS2 9JT, United Kingdom Cataloging-in-Publication data applied for Die Deutsche Bibliothek - CIP-Einheitsaufnahme Logics for concurrency : structure versus automata / Faron Moller; Graham Birtwistle (ed.). - Berlin ; Heidelberg ; New York ; Barcelona ; Budapest ; Hong Kong ; London ; Milan ; Paris ; Santa Clara ; Singapore ; Tokyo Springer, 1996 (Lecture notes in computer science ; Voi. 1043) ISBN 3-540-60915-6 NE: Moiler, Faron [Hrsg.]; GT CR Subject Classification (1991): F.3, F.4, El, F.2 ISBN 3-540-60915-6 Springer-Verlag Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer -Verlag. Violations are liable for prosecution under the German Copyright Law. Springer-Verlag Berlin Heidelberg 1996 Printed in Germany Typesetting: Camera-ready by author SPIN 10512588 06/3142 - 5 4 3 2 1 0 Printed on acid-free paper Preface This volume is a result of the VIII TH BANFF HIGHER ORDER WORKSHOP held from August 27th to September 3rd, 1994, at the Banff Centre in Banff, Canada. -
An Introduction to Johnson–Lindenstrauss Transforms
An Introduction to Johnson–Lindenstrauss Transforms Casper Benjamin Freksen∗ 2nd March 2021 Abstract Johnson–Lindenstrauss Transforms are powerful tools for reducing the dimensionality of data while preserving key characteristics of that data, and they have found use in many fields from machine learning to differential privacy and more. This note explains whatthey are; it gives an overview of their use and their development since they were introduced in the 1980s; and it provides many references should the reader wish to explore these topics more deeply. The text was previously a main part of the introduction of my PhD thesis [Fre20], but it has been adapted to be self contained and serve as a (hopefully good) starting point for readers interested in the topic. 1 The Why, What, and How 1.1 The Problem Consider the following scenario: We have some data that we wish to process but the data is too large, e.g. processing the data takes too much time, or storing the data takes too much space. A solution would be to compress the data such that the valuable parts of the data are kept and the other parts discarded. Of course, what is considered valuable is defined by the data processing we wish to apply to our data. To make our scenario more concrete let us say that our data consists of vectors in a high dimensional Euclidean space, R3, and we wish to find a transform to embed these vectors into a lower dimensional space, R<, where < 3, so that we arXiv:2103.00564v1 [cs.DS] 28 Feb 2021 ≪ can apply our data processing in this lower dimensional space and still get meaningful results. -
Conversational Concurrency Copyright © 2017 Tony Garnock-Jones
CONVERSATIONALCONCURRENCY tony garnock-jones Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy College of Computer and Information Science Northeastern University 2017 Tony Garnock-Jones Conversational Concurrency Copyright © 2017 Tony Garnock-Jones This document was typeset on December 31, 2017 at 9:22 using the typographical look-and-feel classicthesis developed by André Miede, available at https://bitbucket.org/amiede/classicthesis/ Abstract Concurrent computations resemble conversations. In a conversation, participants direct ut- terances at others and, as the conversation evolves, exploit the known common context to advance the conversation. Similarly, collaborating software components share knowledge with each other in order to make progress as a group towards a common goal. This dissertation studies concurrency from the perspective of cooperative knowledge-sharing, taking the conversational exchange of knowledge as a central concern in the design of concur- rent programming languages. In doing so, it makes five contributions: 1. It develops the idea of a common dataspace as a medium for knowledge exchange among concurrent components, enabling a new approach to concurrent programming. While dataspaces loosely resemble both “fact spaces” from the world of Linda-style lan- guages and Erlang’s collaborative model, they significantly differ in many details. 2. It offers the first crisp formulation of cooperative, conversational knowledge-exchange as a mathematical model. 3. It describes two faithful implementations of the model for two quite different languages. 4. It proposes a completely novel suite of linguistic constructs for organizing the internal structure of individual actors in a conversational setting. The combination of dataspaces with these constructs is dubbed Syndicate. -
Symposium on Principles of Database Systems Paris, France – June 14-16, 2004
23ndACM SIGMOD-SIGACT- SIGART Symposium on Principles of Database Systems Paris, France – June 14-16, 2004 http://www.sciences.univ-nantes.fr/irin/SIGMODPODS04/ The symposium invites papers on fundamental aspects of data management. Original research papers on the theory, design, specification, or implementation of data management tools are solicited. Papers emphasizing new topics or foundations of emerging areas are especially welcome. The symposium will be held at the Maison de la Chimie, Paris, in conjunction with the ACM SIGMOD Intl. Conference on Management of Data (SIGMOD’04). Suggested topics include the following (this list is not exhaustive and the order does not reflect priorities): Access Methods & Physical Design Databases & Workflows Real-time Databases Active Databases Deductive Databases & Knowledge Bases Security & Privacy Complexity & Performance Evaluation Distributed Databases Semistructured Data & XML Data Integration & Interoperability Information Processing on the Web Spatial & Temporal Databases Data Mining Logic in Databases Theory of recovery Data Models Multimedia Databases Transaction Management Data Stream Management Object-oriented Databases Views & Warehousing Database Programming Languages Query Languages Web Services & Electronic Commerce Databases & Information Retrieval Query Optimization XML Databases Important Dates: Paper abstracts due: December 1, 2003 (11:59pm PST) Full papers due: December 8, 2003 (11:59pm PST) Notification of acceptance/rejection: February 23, 2004 Camera-ready due: March 22, 2004 -
Self-Sorting SSD: Producing Sorted Data Inside Active Ssds
Foreword This volume contains the papers presentedat the Third Israel Symposium on the Theory of Computing and Systems (ISTCS), held in Tel Aviv, Israel, on January 4-6, 1995. Fifty five papers were submitted in response to the Call for Papers, and twenty seven of them were selected for presentation. The selection was based on originality, quality and relevance to the field. The program committee is pleased with the overall quality of the acceptedpapers and, furthermore, feels that many of the papers not used were also of fine quality. The papers included in this proceedings are preliminary reports on recent research and it is expected that most of these papers will appear in a more complete and polished form in scientific journals. The proceedings also contains one invited paper by Pave1Pevzner and Michael Waterman. The program committee thanks our invited speakers,Robert J. Aumann, Wolfgang Paul, Abe Peled, and Avi Wigderson, for contributing their time and knowledge. We also wish to thank all who submitted papers for consideration, as well as the many colleagues who contributed to the evaluation of the submitted papers. The latter include: Noga Alon Alon Itai Eric Schenk Hagit Attiya Roni Kay Baruch Schieber Yossi Azar Evsey Kosman Assaf Schuster Ayal Bar-David Ami Litman Nir Shavit Reuven Bar-Yehuda Johan Makowsky Richard Statman Shai Ben-David Yishay Mansour Ray Strong Allan Borodin Alain Mayer Eli Upfal Dorit Dor Mike Molloy Moshe Vardi Alon Efrat Yoram Moses Orli Waarts Jack Feldman Dalit Naor Ed Wimmers Nissim Francez Seffl Naor Shmuel Zaks Nita Goyal Noam Nisan Uri Zwick Vassos Hadzilacos Yuri Rabinovich Johan Hastad Giinter Rote The work of the program committee has been facilitated thanks to software developed by Rob Schapire and FOCS/STOC program chairs.