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Algorithms for Reasoning with Probabilistic Graphical Models INTRODUCTORY
Rina Dechter & Alexander Ihler University of California, Irvine Algorithms for Reasoning with Probabilistic Graphical Models INTRODUCTORY Summary The course will cover the primary exact and approximate algorithms for reasoning with probabilistic graphical models (e.g., Bayesian and Markov networks, influence diagrams, and Markov decision processes). We will present inference-based, message passing schemes (e.g., variable elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each algorithm class possesses distinct characteristics and in particular has different time vs. space behavior. We will emphasize the dependence of the methods on graph parameters such as the treewidth, cycle- cutset, and (pseudo-tree) height. We will start from exact algorithms and move to approximate schemes that are anytime, including weighted mini-bucket schemes with cost-shifting and MCMC sampling. Syllabus Class 1: Introduction and Inference schemes • Basic Graphical models – Queries – Examples/applications/tasks – Algorithms overview • Inference algorithms, exact – Bucket elimination – Jointree clustering – Elimination orders • Decomposition Bounds – Dual Decomposition, GDD – MBE/WMBE – Belief Propagation Class 2: Search Schemes • AND/OR search spaces, pseudo-trees – AND/OR search trees – AND/OR search graphs – Generating good pseudo-trees • Heuristic search for AND/OR spaces – Brute-force traversal – Depth-first AND/OR branch and bound – Best-first AND/OR search – The Guiding MBE heuristic – Marginal Map (max-sum-product) • Hybrids of search and Inference Class 3: • Variational methods – Convexity & decomposition bounds – Variational forms & the marginal polytope – Message passing algorithms – Convex duality relationships • Monte Carlo sampling – Basics – Importance sampling – Markov chain Monte Carlo – Integrating inference and sampling References Rina Dechter: Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms. -
Felix Elwert
July 2021 FELIX ELWERT University of Wisconsin–Madison Department of Sociology and Center for Demography and Ecology 4426 Sewell Social Sciences Building 1180 Observatory Drive Madison, WI 53706 [email protected] ACADEMIC POSITIONS University of Wisconsin–Madison 2017- Professor, Department of Sociology Department of Biostatistics and Medical Informatics (affiliated) Department of Population Health Sciences (affiliated) 2013–2017 Associate Professor (Affiliated), Department of Population Health Sciences 2012–2017 Associate Professor, Department of Sociology 2007–2012 Assistant Professor, Department of Sociology WZB Berlin Social Science Center, Germany 2015–2016 Acting Director, Research Unit Social Inequality and Social Policy 2014–2015 Karl W. Deutsch Visiting Professor Harvard Medical School 2006–2007 Postdoctoral Fellow, Department of Health Care Policy EDUCATION 2007 Ph.D., Sociology, Harvard University 2006 A.M., Statistics, Harvard University 1999 M.A., Sociology, New School for Social Research 1997 Vordiplom, Sociology, Free University of Berlin Felix Elwert / May 2020 AWARDS AND FELLOWSHIPS 2018 Leo Goodman Award, Methodology Section, American Sociological Association 2018 Elected member, Sociological Research Association 2018 Vilas Midcareer Faculty Award, University of Wisconsin-Madison 2017- H. I. Romnes Faculty Fellowship, University of Wisconsin-Madison 2016-2018 Fellow, WZB Berlin Social Science Center, Berlin, Germany 2013 Causality in Statistics Education Award, American Statistical Association 2013 Vilas Associate Award, University of Wisconsin–Madison 2012 Jane Addams Award (Best Paper), Community and Urban Sociology Section, American Sociological Association 2010 Gunther Beyer Award (Best Paper by a Young Scholar), European Association for Population Studies 2009, 2010, 2017 University Housing Honored Instructor Award, University of Wisconsin- Madison 2009 & 2010 Best Poster Awards, Annual Meeting of the Population Association of America 2005–2006 Eliot Fellowship, Harvard University 2004 Aage B. -
Separation of Test-Free Propositional Dynamic Logics Over Context-Free Languages
Separation of Test-Free Propositional Dynamic Logics over Context-Free Languages Markus Latte∗ Department of Computer Science University of Munich, Germany For a class L of languages let PDL[L] be an extension of Propositional Dynamic Logic which allows programs to be in a language of L rather than just to be regular. If L contains a non-regular language, PDL[L] can express non-regular properties, in contrast to pure PDL. For regular, visibly pushdown and deterministic context-free languages, the separation of the respective PDLs can be proven by automata-theoretic techniques. However, these techniques introduce non-determinism on the automata side. As non-determinism is also the difference between DCFL and CFL, these techniques seem to be inappropriate to separate PDL[DCFL] from PDL[CFL]. Nevertheless, this separation is shown but for programs without test operators. 1 Introduction Propositional Dynamic Logic (PDL)[9] is a logical formalism to specify and verify programs [12, 16, 11]. These tasks rely on the satisfiability and model-checking problems. Applications in the field are supported by their relatively low complexities: EXPTIME- and PTIME-complete, respectively [9]. Formulas in PDL are interpreted over labeled transition systems. For instance, the formula hpij means that after executing the program p the formula j shall hold. In this context, programs and formulas are defined mutually inductively. This mixture allows programs to test whether or not a formula holds at the current state. Additionally, programs are required to be regular over the set of atomic programs and test operations. For instance, the program while (b) do p; can be rendered as h(b?; p)∗;:bij to ensure that the loop is finite and that j holds when the loop terminates [9]. -
JULY 14-15, 1980 SNOWBIRD, UTAH Princeton, NJ 08544 Charlottesville
MEETING OF CHAIRMEN OF PH.D. GRANTING DEPARTMENTS OF COMPUTER SCIENCE mJ JULY 14-15, 1980 SNOWBIRD, UTAH ATTENDANCE LIST Professor Donald Allison Professor Lee W. Cooprider Dept. of Computer Science Dept. of Computer Science Virginia Polytechnic Inst. Univ. of So. California and State University Los Angeles, CA 90007 Blacksburg, VA 24061 Profesor Saul Amarel Professor Jerome R. Cox, Jr. Dept. of Computer Science Dept. of Computer Science Rutgers University Washington University New Brunswick, NJ 08903 St. Louis, MO 63130 Professor Bruce W. Arden Professor Peter Denning EECS Dept., Eng. Quad. Dept. of Computer Science Princeton University Purdue University Princeton, NJ 08544 West Lafayette, IN 47907 Mr. Bruce Barnes Professor David P. Dobkin Div. Math. & CS. Dept. of Computer Science National Science Foundation The University of Arizona Washington, D.C. 20550 Tucson, AZ 85721 Professor Alan Batson Dr. George G. Dodd DAMACS, Thornton Hall Dept. of Computer University Science of Virginia General Motors Research Lab. Charlottesville, VA 22901 Warren, MI 48090 Professor John Brzozowski Dr. Richard Eckhouse, Jr. Dept. of Computer Science External Research Program University of Waterloo D.E.C.-ML 3/T4l Waterloo, Ontario 146 Main Street Canada N2L 3GI Maynard, MA 01754 Professor William G. Bulgren Professor Gerald Estrin Dept. of Computer Science Dept. of Computer Science University of Kansas 373 Boelter Hall Lawrence, KS 66045 Univ. of CA., Los Angeles Los Angeles, CA 90024 Professor B. F. Caviness Dr. Thelma Estrin Dept. of Math. Science Dept. of Computer Science R.P.I. Univ. of CA., Los Angeles Troy, NY 12181 Los Angeles, CA 90024 Professor S. -
New Probabilistic Model of Stochastic Context-Free Grammars in Greibach Normal Form
New Probabilistic Model of Stochastic Context-Free Grammars in Greibach Normal Form Yevhen Hrubiian Student, group FI-33, Institute of Physics and Technology NTUU “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv, Ukraine [email protected] Імовірнісна Модель Стохастичних Контекстно- Вільних Граматик в Нормальній Формі Грейбах Євген Грубіян Студент групи ФІ-33, Фізико-технічний інститут НТУУ “Киівський політехнічний інститут ім. Ігоря Сікорського” Київ, Україна [email protected] Abstract—In this paper we propose new probabilistic model models of languages were proposed by Claude Shannon to of stochastic context-free grammars in Greibach normal form. describe informational aspects of languages such as entropy but This model might be perspective for future developments and they became very relevant and actual in modern linguistics. N- researching stochastic context-free grammars. gram model is one of the most accurate statistical models of language but the main drawback is that for precious learning Анотація—В роботі запропоновано нову імовірнісну this model for high values of N one needs a huge amount of модель стохастичних контекстно-вільних граматик в texts in different thematic, but unfortunately, even if we have нормальній формі Грейбах. Ця модель може бути got a lot of texts it turns out to be harmful for accurate and перспективною для подальших досліджень стохастичних precise modeling whole language. Because of this reason контекстно–вільних граматик. stochastic context-free grammars were introduced for more Keywords—stochastic context-free grammar; Greibach normal precious description of languages because context-freeness form; hidden Markov models guarantees grammatical pattern of language and probability distributions on grammatical rules approach semantic structure Ключові слова—стохастична контекстно-вільна граматика; of phrases. -
Rina Dechter
Rina Dechter Information and Computer Science Irvine, CA 92717 email: [email protected] www.ics.uci.edu/~ dechter 949-824-6556 Education • University of California, Los Angeles Computer Science, Major Field: Artificial Intelligence Ph.D. 1985 • Weizman Institute of Science, Rehovot, Israel Applied Mathematics M.S. 1976 • The Hebrew University of Jerusalem, Israel Mathematics and Statistics B.S. 1973 Work Experience • University of California, Irvine Information and Computer Science July 1996{present: Full Professor July 1992{1996: Associate Professor Oct 1990{1992: Assistant Professor • Technion, Israel Computer Science Department Oct 1988 - Sept 1990: Senior Lecturer, (equivalent to Assistant Profes- sor) 1 • Hughes Aircraft, Calabasas July 85 - Mar 88: Staff member, AI Center • University of California, Los Angeles Computer Science Department Oct 87 - Sept 88: Research Associate • University of California, Los Angeles Computer Science Department July 85 - Sept 87: Presidential Postdoctoral Scholar • IBM Scientific Center, Los Angeles, CA Sept 82 - Dec 84: Student member of Technical Staff, Robotic Project • University of California, Los Angeles Computer Science Department July 81 - Sept 85: Research Assistant Research in the areas of Communication Networks (1981-1982), and Artificial Intelligence (1982-85) • Perceptronics Inc. Woodland Hills, California Oct 78 - Mar 80: Research Mathematician Responsible for modeling, experimental design and computer simula- tions for Human-Resource Test and Evaluation for advanced weapon systems • Everyman's University, Tel-Aviv, Israel Apr 76 - Aug 78: Staff member Developed self-study programs for teaching college mathematics. Awards and Honors • 1986-87: UC Presidential Postdoctoral Fellowship. • 1990: Best Paper, Canadian AI Conference (CSCSI-90) for [C15,J10] • 1991: NSF Presidential Young Investigator. -
Department of Computer Science
University of California, Irvine 2016-2017 1 Department of Computer Science Alexandru Nicolau, Department Chair Rina Dechter, Department Vice Chair 3019 Donald Bren Hall 949-824-1546 http://www.cs.uci.edu/ Overview With 40 full-time faculty members, 300+ graduate students, and more than 2,000 undergraduates, we provide a world-class research environment spanning not only the core areas of computer science — including computer architecture, system software, networking and distributed computing, data and information systems, the theory of computation, artificial intelligence, and computer graphics — but also highly interdisciplinary programs, such as biomedical informatics, data mining, security and privacy, and ubiquitous computing. The diverse research interests of our faculty are reflected directly in our educational programs. Computer Science faculty teach most of the undergraduate and graduate courses for the degree programs in both Computer Science and Information & Computer Science. We jointly offer with our colleagues in The Henry Samueli School of Engineering an undergraduate degree in Computer Science and Engineering, as well as the graduate program in Networked Systems. We also have a major in Computer Game Science, jointly offered with the Department of Informatics. Our department collaborates with many other institutions in the United States and abroad, and its doors are always open to a multitude of visitors and collaborators from all corners of the globe. Undergraduate Major in Computer Science The Computer Science major emphasizes the principles of computing that underlie our modern world, and provides a strong foundational education to prepare students for the broad spectrum of careers in computing. This major can serve as preparation for either graduate study or a career in industry. -
The UCLA Fixed-Plus-Variable (F+V) Structure Computer
Reconfigurable Computer Origins: The UCLA Fixed-Plus-Variable (F+V) Structure Computer Gerald Estrin University of California at Los Angeles Gerald Estrin and his group at the University of California at Los Angeles did the earliest work on reconfigurable computer architectures. The early research, described here, provides pointers to work on models and tools for reconfigurable systems design and analysis. The earliest work on reconfigurable computer Pasta’s challenge architecture was done at the University of What triggered the UCLA work on reconfig- California at Los Angeles (UCLA), as a 1999 urable computer systems? In the spring of report in The Economist noted (see “The 1959, John Pasta, a highly respected applied Economist: Reconfigurable Systems Undergo mathematician and physicist and chairman of Revival” sidebar, p. 4). Clearly, in retrospect, the Mathematics and Computer Science digital technology was not ready for such a rev- Research Advisory Committee to the Atomic olutionary change. Now that digital technolo- Energy Commission, expressed concern about gy is enabling commercial realization, however, many vital computational problems whose the time seems right to tell this story. Of equal solutions were beyond the capabilities of exist- interest might be the saga of how a professor ing electronic computers. In his opinion, com- and his graduate students, in an academic set- mercial computer manufacturers had lost ting, can work on one complex problem over a interest in exploring risky, innovative comput- prolonged period and bring to the computer er architectures. Instead, the manufacturers science world original ideas that can them- wanted to serve the growing market for con- selves be commercially realized. -
Donald Bren School of Information and Computer Sciences Donald Bren Hall Dedication
Annual Report 2006-07 Donald Bren School of Information and Computer Sciences Donald Bren Hall Dedication. June 20, 2007. (L to R) Vice Chancellor Tom Mitchell, Brenda Drake, Chancellor Michael V. Drake, Donald Bren, and Dean Debra J. Richardson Dear Bren School Community, 2006-07 was an exciting academic year at UC Irvine’s Donald Bren School of Information and Computer Sciences. We had many successes and firsts as we moved into the new Donald Bren Hall and welcomed many new graduate and undergraduate students. Today, we are an academic community of more than 1,500 students, over 100 full-time faculty and staff, and approximately 7,000 alumni worldwide. In teaching and scholarship, we continue to be among the top in information and computer sciences. To add to our list of accolades, our Networked Systems program was just rated number one by Academic Analytics, in additino to being ranked third in Information Systems. 04 Bren School Faculty At the Bren School, we have a unique perspective on computing and 08 Department of Computer Science information technology, stimulating society daily. Our vibrant community, 10 Department of Informatics comprised of researchers and educators as well as industry-leading scholars, 12 Department of Statistics explores innovative topics ranging from building complete computer systems on 14 Center of Excellence chips smaller than a finger nail to developing user interface systems that allow 18 Student Affairs engineers on opposite sides of the world to collaborate effectively. 20 Business Office I invite you stay in touch with us throughout the year by subscribing to our RSS 21 Communications feed or visiting our Web site regularly (www.ics.uci.edu). -
CV-Prof.-Rachel-Ben-Eliyahu-Zohary
CURRICULUM VITAE Name: Ben Eliyahu- Zohary Rachel 1. Academic Education B.Sc. 1983, Mathematics and Computer Science, Hebrew University of Jerusalem, Jerusalem, Israel. Ph.D. 1993, Computer Science University of California, Los-Angeles, UCLA. Topic of Dissertation: Computational aspects of nonmonotonic Reasoning. Advisors: Professor Judea Pearl (UCLA) and Professor Rina Dechter (UCI). 2. Academic Employment 2006-present Associate Professor, Jerusalem College of Engineering, Jerusalem, Israel. 2003-present Head, Department of Software Engineering, Jerusalem College of Engineering, Jerusalem, Israel. (on sabbatical 2004-2005, 2012-2013) 2004-2005 Research and Teaching, Harvard Division of Engineering and Applied Sciences, Visiting Scholar. 2003-2006 Senior Lecturer, Jerusalem College of Engineering, Jerusalem, Israel. 2000-2010 Senior Lecturer, Department of Communication Systems Engineering, Ben- Gurion University of the Negev, Beer-Sheva, Israel. (on maternity leave 2002, on leave 2003-2006) 2000-2003 Head of study Programs Committee, Ben-Gurion University of the Negev, Beer-Sheva, Israel. 1995-1998 Lecturer, Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel. 1995-1998 Head of study Programs Committee, Ben-Gurion University of the Negev, Beer-Sheva, Israel. 1993-1995 Post-doctoral researcher, Faculty of Computer Science, Technion - Israel Institute of Technology, Haifa, Israel. 1990-1993 Research Assitant, Department of Computer Science - Cognitive Systems Laboratory, UCLA. 1988-1990 Teaching Assistant -
Practical Tractability of CSPS by Higher Level Consistency and Tree Decomposition
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Computer Science and Engineering: Theses, Computer Science and Engineering, Department Dissertations, and Student Research of Spring 5-3-2013 Practical Tractability of CSPS by Higher Level Consistency and Tree Decomposition Shant Karakashian University of Nebraska-Lincoln, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/computerscidiss Part of the Artificial Intelligence and Robotics Commons Karakashian, Shant, "Practical Tractability of CSPS by Higher Level Consistency and Tree Decomposition" (2013). Computer Science and Engineering: Theses, Dissertations, and Student Research. 58. https://digitalcommons.unl.edu/computerscidiss/58 This Article is brought to you for free and open access by the Computer Science and Engineering, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Computer Science and Engineering: Theses, Dissertations, and Student Research by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. PRACTICAL TRACTABILITY OF CSPS BY HIGHER LEVEL CONSISTENCY AND TREE DECOMPOSITION by Shant Karakashian A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfilment of Requirements For the Degree of Doctor of Philosophy Major: Computer Science Under the Supervision of Professor Berthe Y. Choueiry Lincoln, Nebraska May, 2013 PRACTICAL TRACTABILITY OF CSPS BY HIGHER LEVEL CONSISTENCY AND TREE DECOMPOSITION Shant Karakashian, Ph. D. University of Nebraska, 2013 Adviser: Berthe Y. Choueiry Constraint Satisfaction is a flexible paradigm for modeling many decision problems in Engineering, Computer Science, and Management. Constraint Satisfaction Problems (CSPs) are in general N P -complete and are usually solved with search. -
Describing Automata Associatedwiththei Interms
. DESCRIBING AUTOMATA INTERMS OF-LANGUAGES ASSOCIATEDWITHTHEI R PERIPHERAL DEVICES . Reino Kurki-Suonio STAN-CS-75-493 MAY 1975 COMPUTER SCIENCE DEPARTMENT School of Humanities and Sciences STANFORD UNIVERSITY Describing Automata in Terms of Languages Associated with Their Peripheral Devices bY Reino Kurki-Suonio Computer Science Department Stanford University and -- University of Tampere, Finland Abstract A unified approach is presented to deal with automata having different kinds of peripheral devices. This approach is applied to pushdown automata and Turing machines, leading to elementary proofs of several well-known theorems concerning transductions, relationship between pushdown automata and context-free languages, as well as homomorphic characterization and undecidability questions. a In general, this approach leads to homomorphic characterization of language families generated by a single language by finite transduction. This research was supported in part by National Science Foundation grant ($I 36473~ and by the Academy of Finland. Reproduction in whole or in part is pmmitted for any purpose of the United States Government. 1 1. Introduction Mathematical formulations of various classes of automata do not usually allow uniform treatment of different kinds of automata. One reason is in irrelevant differences of conventions. For instance, input may come from an input source, or it may constitute the initial contents of some storage device. Also, the formalisms which are used may be difficult to manage in a general situation. Functional notations, for instance, become quite clumsy to use when several storage devices are introduced. L The purpose of this paper is to present an approach, suggested by Floyd [l], where several kinds of automata are treated in a uniform way.