DOCSLIB.ORG

Conference Program

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Conference Program The Third International Conference on Knowledge Discovery and Data Mining August 14–17 1997, Newport Beach, California Sponsored by the American Association for Artificial Intelligence Cosponsored by General Motors Research, The National Institute for Statistical Sciences, and NCR Corporation In cooperation with The American Statistical Association Welcome to KDD-97! KDD-97 Organization General Conference Chair William Eddy, Carnegie Mellon Sally Morton, Rand Corporation University Richard Muntz, University of California Ramasamy Uthurusamy, General Motors Charles Elkan, University of California at at Los Angeles Corporation San Diego Raymond Ng, University of British Usama Fayyad, Microsoft Research Columbia Program Cochairs Ronen Feldman, Bar-Ilan University, Steve Omohundro, NEC Research David Heckerman, Microsoft Research Israel Gregory Piatetsky-Shapiro, Heikki Mannila, University of Helsinki, Jerry Friedman, Stanford University GTE Laboratories Finland Dan Geiger, Technion, Israel Daryl Pregibon, Bell Laboratories Daryl Pregibon, AT&T Laboratories Clark Glymour, Carnegie Mellon Raghu Ramakrishnan, University of University Wisconsin, Madison Publicity Chair Moises Goldszmidt, SRI International Patricia Riddle, Boeing Computer Georges Grinstein, University of Services Paul Stolorz, Jet Propulsion Laboratory Massachusetts, Lowell Ted Senator, NASD Regulation Inc. Tutorial Chair Jiawei Han, Simon Fraser University, Jude Shavlik, University of Wisconsin at Canada Madison Padhraic Smyth, University of California, David Hand, Open University, UK Wei-Min Shen, University of Southern Irvine Trevor Hastie, Stanford University California David Heckerman, Microsoft Corpora- Arno Siebes, CWI, Netherlands Demo and tion Avi Silberschatz, Bell Laboratories Poster Sessions Chair Haym Hirsh, Rutgers University Evangelos Simoudis, IBM Almaden Jim Hodges, University of Minnesota Research Center Tej Anand, NCR Corporation Se June Hong, IBM T .J. Watson Research Andrzej Skowron, University of Warsaw Awards Chair Center Padhraic Smyth, University of California Tomasz Imielinski, Rutgers University at Irvine Gregory Piatetsky-Shapiro, Yannis Ioannidis, University of Wisconsin Ramakrishnan Srikant, IBM Almaden GTE Laboratories Lawrence D. Jackel, AT&T Laboratories Research Center David Jensen, University of Massachusetts John Stasko, Georgia Institute of Technol- Program Committee Members Michael Jordan, Massachusetts Institute ogy Helena Ahonen, University of Helsinki of Technology Salvatore J. Stolfo, Columbia University Tej Anand, NCR Daniel Keim, University of Munich Paul Stolorz, Jet Propulsion Laboratory Ron Brachman, AT&T Laboratories Willi Kloesgen, GMD, Germany Hanna Toivonen, University of Helsinki Carla Brodley, Purdue University Ronny Kohavi, Silicon Graphics Alexander Tuzhilin, New York University, Dan Carr, George Mason University David D. Lewis, AT&T Laboratories – Stern School Peter Cheeseman, Research Ramasamy Uthurusamy, General Motors NASA AMES Research Center David Madigan, University of R&D Center David Cheung, University of Hong Kong Washington Graham Wills, Bell Laboratories Wesley Chu, University of California at Heikki Mannila, University of Helsinki David Wolpert, IBM Almaden Research Los Angeles Brij Masand, GTE Laboratories Center Gregory Cooper, University of Pittsburgh Gary McDonald, General Motors Jan Zytkow, Wichita State University Robert Cowell, City University, UK Research Bruce Croft, University of Massachusetts Eric Mjolsness, University of California at Amherst at San Diego 2 KDD–97 Thursday, August 14th 8:00AM–6:00 PM 1:30–3:30 PM 4:00–6:00 PM Tutorials Tutorial 4 Tutorial 7 Exploratory Data Analysis Using Statistical Models for Categorical 8:00–10:00 AM (SINGLE SESSION) Interactive Dynamic Graphics Response Data Tutorial 1 Deborah Swayne, Bell Communications Re- William DuMouchel, AT&T Research Data Mining and KDD: search and Diane Cook, Iowa State University An Overview 6:30–7:30 PM Usama Fayyad, Microsoft Research and Opening Reception Evangelos Simoudis, IBM 1:30–3:30 PM Tutorial 5 California Ballroom, Newport Beach Mar- 10:00–10:30 AM OLAP and Data Warehousing riott Hotel and Tennis Club Coffee Break Surajit Chaudhuri, Microsoft Research and Umesh Dayal, Hewlett Packard Laboratories 10:30 AM–12:30 PM Tutorial 2 3:30–4:00 PM Modeling Data and Discovering Coffee Break Knowledge David Hand, Open University, UK 4:00–6:00 PM Tutorial 6 10:30 AM–12:30 PM Visual Techniques for Exploring Tutorial 3 Databases Text Mining - Theory and Practice Daniel Keim, University of Munich, Ger- Ronen Feldman, Bar-Ilan University, many Israel 12:30–1:30 PM Lunch KDD-97 Tutorial Program All tutorials will be presented on Thursday, August 14, 1997 in sections of the Pacific Ballroom. Admission to the tutorials is included in your conference registration fee. Registrants can attend up to four consecutive tutorials, including four tutorial syllabi. TIME SESSION 1 SESSION 2 8:00 to 10:00 AM T1 – Fayyad and Simoudis (single session) 10:30 AM to 12:30 PM T2 – Hand T3 – Feldman 1:30 to 3:30 PM T4 – Swayne and Cook T5 – Chaudhuri and Dayal 4:00 to 6:00 PM T6 – Keim T7 – DuMouchel KDD–97 3 Friday, August 15th All sessions are plenary sessions and will be 10:30–12:15 PM 11:15 AM–12:15 PM held in the Pacific Ballroom, Newport Beach Keynote Address and Poster Previews Session Marriott Hotel and Tennis Club Poster Previews Session (Authors A-J) 12:15–2:00 PM 8:30–10:00 AM 10:30–11:15 AM Keynote Address and Keynote Address: Searching Lunch for Causes and Predicting Invited Talk Session Interventions (Program Committee Luncheon, New- Clark Glymour, University of California port North Room, lobby level, Newport 8:30 –8:45 AM San Diego and Carnegie Mellon University Beach Marriott Hotel and Tennis Club) Welcome and Introduction 2:00–3:00 PM KDD-97 General Conference Chair: The problem in many data mining tasks Poster Previews Session Ramasamy Uthurusamy and KDD-97 Pro- is to predict features of a new, unob- gram Cochairs: David Heckerman, Heikki (Authors K-Z) Mannila, and Daryl Pregibon served sample from a recorded sample, assuming the sampling distribution 3:00–7:00 PM does not change. In causal data mining Exhibits, Demos, Poster Sessions 8:45–9:30 AM the task is to predict the features of new Keynote Address: From Large to unobserved samples from a recorded California Ballroom, Newport Beach Huge: A Statistician’s Reactions to sample, assuming any unobserved sam- Marriott Hotel and Tennis Club KDD & DM ple results from an intervention which 3:00 –4:20 PM Peter J. Huber, University of Bayreuth, Ger- directly alters the underlying probabili- many Poster Session 1 ty distribution of some variables in a known way, and alters others indirectly Authors A-G The statistics and AI communities are through the influences of the directly confronted by the same challenge—the 4:20 –5:40 PM manipulated variables. Data mining in onslaught of ever larger data collec- the sciences and for business, military Poster Session 2 tions—but the two communities have or public policy is often concerned with Authors H-O reacted independently and differently. discovering causal structure, although What could they learn from each other that focus is sometimes obscured by the 5:40 –7:00 PM if they looked over the fence? What is belief that causation is especially myste- Poster Session 3 amiss on either side? rious. Causation is not probability, but Authors P-Z 9:30–10:00 AM it is no more (and no less) mysterious. (Coffee and soft drinks will be available Invited Talk: Machine Learning and This lecture will illustrate why data in the foyer from 3:00–7:00 PM.) KDD: New Developments mining for causes using techniques de- Thomas Dietterich, University of Oregon signed for recognition or classification is unwise, and will illustrate principles Machine learning research is pursuing of causal data mining with several in- many directions relevant to KDD. This teresting cases that use both Bayesian talk will review two exciting lines of re- and constraint based methods. search: learning with ensembles (com- mittees, bagging, boosting, etc.) and learning with stochastic models. I will also briefly mention current research in reinforcement learning and methods for scaling up machine learning algo- rithms. 10:00–10:30 AM Coffee Break 4 KDD–97 Saturday, August 16th 8:30–9:45 AM 11:00–11:30 AM Propulsion Laboratory, California Institute Paper Session Invited Talk: Highlights from the of Technology; Andrew Fraser, Portland State Database Methodology Joint Statistical Meetings University Jim Hodges, University of Minnesota 8:30–8:55 AM 2:30–3:00 PM Computing Optimized Rectilinear Statisticians and data-miners have a lot in Automated Discovery of Active Regions for Association Rules common but their paths tend not to cross. Motifs in Three Dimensional Kunikazu Yoda, Takeshi Fukuda, Yasuhiko Thus, for example, many statisticians are Molecules missing out on computing and other ideas Morimoto, Shinichi Morishita, and Takeshi Xiong Wang and Jason T.L. Wang, New Jer- familiar to data-miners, and data-miners Tokuyama, IBM Tokyo Research Laboratory, sey Institute of Technology; Dennis Shasha, sometimes reinvent things that are well- Japan New York University; Bruce Shapiro, Nation- known to statisticians. This talk is intended al Cancer Institute; Sitaram Dikshitulu, New to toss a small bridge across the gap, by Jersey Institute of Technology; Isidore Rigout- 8:55–9:20 AM summarizing some talks and trends from sos, IBM T. J. Watson Research Center; Density-Connected Sets and their the 1997
Load more

Recommended publications
  • The Impotent Fury of William Dembski
    THE DESIGN REVOLUTION? How William Dembski Is Dodging Questions About Intelligent Design By Mark Perakh Who is William A. Dembski? We are told that he has PhD degrees in mathematics and philosophy plus more degrees - in theology and what not – a long list of degrees indeed. [1] To acquire all those degrees certainly required an unconventional penchant for getting as many degrees as possible. We all know that degrees alone do not make a person a scientist. Scientific degrees are not like ranks in the military where a general is always above a mere colonel. Degrees are only a formal indicator of a person’s educational status. A scientist’s reputation and authority are based on his degrees only to a negligible extent. What really attests to a person’s status in science is publications in professional journals and anthologies and references to one’s work by colleagues. This is the domain where Dembski has so far remained practically invisible. All his multiple publications have little or nothing to do with science. He is a mathematician who did not prove any theorem and derived not a single formula. When he writes about probability theory or information theory -- on which he is proclaimed to be an expert -- the real experts in these fields (using the words of the prominent mathematician David Wolpert) “squint, furrow one's brows, and then shrug.” [2] When encountering critique of his work, Dembski is selective in choosing when to reply to his critics and when to ignore their critique. His preferred targets for replies are those critics who do not boast comparable long lists of formal credentials – this enables him to contemptuously dismiss the critical comments by pointing to the alleged lack of qualification of his opponents while avoiding answering the essence of their critical remarks.
    [Show full text]
  • Multi-Agent Economics and the Emergence of Critical Markets
    Multi-agent Economics and the Emergence of Critical Markets Michael S. Harr´e1 1Complex Systems Research Group, Faculty of Engineering and IT, The University of Sydney, Sydney, Australia. Abstract The dual crises of the sub-prime mortgage crisis and the global financial crisis has prompted a call for explanations of non-equilibrium market dynamics. Recently a promising approach has been the use of agent based models (ABMs) to simulate aggregate market dynamics. A key aspect of these models is the endogenous emergence of critical transitions between equilibria, i.e. market collapses, caused by multiple equilibria and changing market parameters. Several research themes have developed microeconomic based models that include multiple equilibria: social decision theory (Brock and Durlauf), quantal response models (McKelvey and Palfrey), and strategic complementarities (Goldstein). A gap that needs to be filled in the literature is a unified analysis of the relationship between these models and how aggregate criticality emerges from the individual agent level. This article reviews the agent-based foundations of markets starting with the individual agent perspective of McFadden and the aggregate perspective of catastrophe theory emphasising connections between the different approaches. It is shown that changes in the uncertainty agents have in the value of their interactions with one another, even if these changes are one-sided, plays a central role in systemic market risks such as market instability and the twin crises effect. These interactions can endogenously cause crises that are an emergent phenomena of markets. 1 Introduction Multi-agent models have grown in popularity [2, 5, 64, 9, 34, 37] as a way in which to simu- late complex market dynamics that might have no closed form solutions.
    [Show full text]
  • Big Data Everywhere, and No SQL in Sight Editorial: from SIGKDD Chairman Usama M
    Big Data Everywhere, and No SQL in Sight Editorial: from SIGKDD Chairman Usama M. Fayyad Chairman, SIGKDD Executive Committee Executive Chairman, Oasis500 – www.oasis500.com Chairman & CTO, Blue Kangaroo (ChoozOn Corp) [email protected] ABSTRACT traditional data storage and management state-of-the-art. Usually these limits are tested on 3 major fronts: the 3-V’s: Volume, As founding editor-in-chief of this publication, I used to Velocity, and Variety. I like to talk about the 4-V’s of data systematically address our readers via regular editorials. In mining, where the 4th V stands for Value: delivering insights and discussions with the current Editor-in-Chief, Bart Goethals, we business value from the data. More on this topic in a future decided that it may be a good idea to restore the regular editorial Editorial. For now, I would like to focus on some of the section from me as SIGKDD Chairman/director. So this is a first opportunities and threats arising from this developing situation. in what hopefully will be a regularly occurring event. This Editorial contribution discusses the issues facing the fields of data 2. BREAKDOWN IN THE RELATIONAL mining and knowledge discovery, including challenges, threats DATABASE WORLD and opportunities. In this Editorial we focus on the emerging area While most relational database researchers and practitioners are of BigData and the concerns that we are not contributing at the still living in denial about what is going on, what I am witnessing level we should to this important field. We believe we have a is nothing short of a major breakdown.
    [Show full text]
  • Organization List
    KDD’13 Conference on Knowledge Discovery & Data Mining Organization List General Co-Chairs: Robert L. Grossman, Univ. of Chicago and Open Data Group Ramasamy Uthurusamy, General Motors Corporation, Ret. Program Co-Chairs: Inderjit S. Dhillon, University of Texas Yehuda Koren, Google Industry & Government Track Co-Chairs: Rayid Ghani, University of Chicago Ted E. Senator, SAIC Industry Practice Expo Co-Chairs: Paul Bradley, MethodCare Rajesh Parekh, Groupon Workshop Co-Chairs: Jian Pei, Simon Fraser University Jie Tang, Tsinghua University Tutorial Co-Chairs: W. Scott Spangler, IBM Research Zhi-Hua Zhou, Nanjing University Local Arrangements Chair: Bamshad Mobasher, DePaul University Meira Leonard, University of Chicago Exhibit and Demo Co-Chairs: Natasha Balac, San Diego Supercomputer Center, UCSD Hui Xiong, Rutgers University SIGKDD 2013 Awards Chair: Ying Li, Concurix Corporation SIGKDD 2013 Doctoral Dissertation Awards Chair: Bamshad Mobasher, DePaul University KDD Cup Chairs: Brian Dalessandro, Media6Degrees Claudia Perlich, Media6Degrees Ben Hamner, Kaggle William Cukierski, Kaggle Media and Publicity Co-Chairs: Ankur Teredesai, Univ of Washington – Tacoma Emilia Palaveeva, Yantra PR Panel Co-Chairs: Foster Provost, New York University Geoff Webb, Monash University Publications Co-Chairs: Jingrui He, Stevens Institute of Technology Jimeng Sun, IBM TJ Watson Research Center Social Network Co-Chairs: Gabor Melli, VigLink Inc Ron Bekkerman, Carmel Ventures Sponsorship Co-Chairs: Dou Shen, Baidu Michael Zeller, Zementis xxii Treasurer:
    [Show full text]
  • What Is Important About the No Free Lunch Theorems? 3
    What is important about the No Free Lunch theorems? David H. Wolpert Abstract The No Free Lunch theorems prove that under a uniform distribution over induction problems (search problemsor learning problems), all induction algorithms perform equally. As I discuss in this chapter, the importanceofthe theoremsarises by using them to analyze scenarios involving non-uniform distributions, and to compare different algorithms, without any assumption about the distribution over problems at all. In particular, the theorems prove that anti-cross-validation (choosing among a set of candidate algorithms based on which has worst out-of-sample behavior) performs as well as cross-validation, unless one makes an assumption — which has never been formalized — about how the distribution over induction problems, on the one hand,is relatedto the set of algorithmsone is choosingamong using (anti-)cross validation, on the other. In addition, they establish strong caveats concerning the significance of the many results in the literature which establish the strength of a particular algorithm without assuming a particular distribution. They also motivate a “dictionary” between supervised learning and improve blackbox optimization, which allows one to “translate” techniques from supervised learning into the domain of blackbox optimization, thereby strengthening blackbox optimization algorithms. In addition to these topics, I also briefly discuss their implications for philosophy of science. 1 Introduction arXiv:2007.10928v1 [cs.LG] 21 Jul 2020 The first of what are now called the No Free Lunch (NFL) theorems were published in [1]. Soon after publication they were popularizedin [2], building on a preprint ver- sion of [1]. Those first theorems focused on (supervised) machine learning.
    [Show full text]
  • David Wolpert Research Statement
    David Wolpert Research Statement Although my degrees were in physics (Princeton, then the Kavli Institute for Theoretical Physics), my interests are far broader. Reflecting this I have worked at the Santa Fe Institute, the Neurosciences Institute at Rockefeller University, Los Alamos’ Center for Nonlinear Studies, TXN (a data-mining startup), and IBM. Currently I am a senior computer scientist at NASA. Reflecting this breadth of my interests, I have published work on the following topics, most of it being on the first four: Multi-agent systems and distributed control Operations research (in particular nonlinear and heuristic optimization) Machine learning Statistics (Bayesian, Sampling theory, and Maxent) Nanotechnology Molecular biology Computation theory Game theory Several branches of mathematics. In the last few years my research focus has been five-fold: 1) PROBABILITY COLLECTIVES: By casting both of them in terms of information theory, game theory and statistical physics are, mathematically, identical. Intuitively, players in a game and their reward functions are identical to particles in a system and their energy functions. This identity allows one to “cut and paste” techniques between these fields, creating a hybrid richer than either by itself. So for example, the proper quantification of the bounded rationality of an agent turns out to be formally identical to a particle’s temperature. As another example, techniques in statistical physics used to analyze systems with varying numbers of particles can be applied to games with varying numbers of agents. Along with the members of my group and collaborators at Oxford, Stanford, Berkeley, and Los Alamos, I have been investigating the extremely rich theory arising from this hybridization.
    [Show full text]
  • LA-UR-11-06242 Approved for Public Release; Distribution Is Unlimited
    LA-UR-11-06242 Approved for public release; distribution is unlimited. Title: Modeling Humans as Reinforcement Learners: How to Predict Human Behavior in Multi-Stage Games Author(s): Ritchie Lee David Wolpert Scott Backhaus Russell Bent James Bono Brendan Tracey Intended for: 25th Annual Conference on Neural Information Processing Systems, Workshop on Decision Making with Multiple Imperfect Decision Makers, Granda, Spain, December 2011 Los Alamos National Laboratory, an affirmative action/equal opportunity employer, is operated by the Los Alamos National Security, LLC for the National Nuclear Security Administration of the U.S. Department of Energy under contract DE-AC52-06NA25396. By acceptance of this article, the publisher recognizes that the U.S. Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution, or to allow others to do so, for U.S. Government purposes. Los Alamos National Laboratory requests that the publisher identify this article as work performed under the auspices of the U.S. Department of Energy. Los Alamos National Laboratory strongly supports academic freedom and a researcher’s right to publish; as an institution, however, the Laboratory does not endorse the viewpoint of a publication or guarantee its technical correctness. Form 836 (7/06) Modeling Humans as Reinforcement Learners: How to Predict Human Behavior in Multi-Stage Games Ritchie Lee David H. Wolpert Carnegie Mellon University Silicon Valley Intelligent Systems Division NASA Ames Research Park MS23-11 NASA Ames Research Center MS269-1 Moffett Field, CA 94035 Moffett Field, CA 94035 [email protected] [email protected] Scott Backhaus Russell Bent Los Alamos National Laboratory Los Alamos National Laboratory MS K764, Los Alamos, NM 87545 MS C933, Los Alamos, NM 87545 [email protected] [email protected] James Bono Brendan Tracey Department of Economics Department of Aeronautics and Astronautics American University Stanford University 4400 Massachusetts Ave.
    [Show full text]
  • Thèses Et HDR 2015
    UMR 5205 CNRS Thèses et HDR 2015 Laboratoire d’InfoRmatique en Image et Systèmes d’information Sommaire HDR Graphes et Jeux combinatoires Eric Duchêne ............................................................................................................................. 7 Des pixels à la signification Elöd Egyed-Zsigmond ............................................................................................................ 11 Analyse des usages des plateformes de construction de connaissances par des méthodes mixtes et réflexives pour l’amélioration de l’appropriation et de la structuration de l’information Christine Michel ...................................................................................................................... 17 Thèses Data collection optimization in Wireless Sensor Networks, application to the Everblu smart metering Network Besem Abid ............................................................................................................................. 21 Web services composition in uncertain environments Soumaya Amdouni ................................................................................................................. 23 Automatic Prediction of Emotions Induced by Movies Yoann Baveye .......................................................................................................................... 25 Configuration automatique d’un solveur générique intégrant des techniques de décomposition arborescente pour la résolution de problèmes de satisfaction de contraintes
    [Show full text]
  • Ensemble Learning for Ranking Interesting Attributes
    Graduate Theses, Dissertations, and Problem Reports 2005 Ensemble learning for ranking interesting attributes Erik W. Sinsel West Virginia University Follow this and additional works at: https://researchrepository.wvu.edu/etd Recommended Citation Sinsel, Erik W., "Ensemble learning for ranking interesting attributes" (2005). Graduate Theses, Dissertations, and Problem Reports. 1685. https://researchrepository.wvu.edu/etd/1685 This Thesis is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [email protected]. Ensemble Learning for Ranking Interesting Attributes Erik W. Sinsel Thesis submitted to the College of Engineering and Mineral Resources at West Virginia University in partial fulfillment of the requirements for the degree of Master of Science in Computer Science Bojan Cukic, Ph.D., Chair Frances Van Scoy, Ph.D. John Atkins, Ph.D. Lane Department of Computer Science and Electrical Engineering Morgantown, West Virginia 2005 Abstract Machine learning knowledge representations, such as decision trees, are often incomprehensible to humans. They can also contain errors specific to the representation type and the data used to generate them.
    [Show full text]
  • Session at AAAS Annual Meeting
    SESSION on DESIGN THINKING TO MOBILIZE SCIENCE, TECHNOLOGY AND INNOVATION FOR SOCIAL CHALLENGES The aim of this symposium is to highlight the innova- tive approaches towards address social challenges. There has been growing interest in promoting “social innovation” to imbed innovation in the wider economy by fostering opportunities for new actors, such as non-profit foundations, to steer research and collaborate with firms and entrepreneurs and to tackle social challenges. User and consumers are also relevant as they play an important role in demanding innovation for social goals but also as actors and suppliers of solutions. Although the innovation process is now much more open and receptive to social influences, progress on social innovation will call for the greater involvement of stakeholders who can mobilize science, technol- ogy and innovation to address social challenges. Thus, the session requires to be approached from holistic and multidisciplinary mind and needs to cover the issue from different aspects by seven inter- national speakers. Contents Overview ■1 Program ■4 Session Description ■6 Outline of Presentation ■7 Lecture material ■33 Appendix ■55 Program Welcome and Opening 8:30-8:35 • Dr. Yuko Harayama, Deputy Director of the OECD’s Directorate for Science, Technology and Industry (DSTI) ● (moderator) (5min) Session 1: Putting ‘design thinking’ into practice 8:35-9:20 • Dr. Karabi Acharya, Change Leader, Ashoka, USA(15min) ● Systemic Change to Achieve Environmental Impact and Sustainability • Mr. Tateo Arimoto, Director-General, Research Institute of Science and Technology for Society, Japan Science and Technology Agency (JST / RISTEX) (Co-organiser and Host), Japan (15min) Design Thinking to Induce new paradigm for issue-driven approach −Discussant (5min) • Dr.
    [Show full text]
  • London, United Kingdom August 19 ‑ 23, 2018 24Th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
    London, United Kingdom August 19 ‑ 23, 2018 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining PRELIMINARY PROGRAM Contents KDD 2018 Agenda at a Glance KDD 2018 Chairs’ Welcome Message Program Highlights KDD 2018 Tutorial Program KDD 2018 Health Day Program KDD 2018 Workshop Program KDD 2018 Deep Learning Day Program KDD 2018 Hands‑On Tutorial Program KDD Cup 2018 Program KDD 2018 Project Showcase Program KDD 2018 Conference Program KDD 2018 Conference Organization KDD 2018 Sponsors & Supporters Useful Links and Emergency Contacts KDD 2018 Agenda at a Glance KDD 2018: Sunday, August 19 (TUTORIAL DAY) 7:00AM ‑ 5:00PM KDD 2018 Registration Boulevard (Level 0) 8:00AM ‑ 12:00PM T1: Graph and Tensor Mining for Fun and Profit ICC Capital Suite Room 7 (Level 3) T2: Privacy‑preserving Data Mining in Industry: Practical 8:00AM ‑ 12:00PM Challenges and Lessons Learned ICC Capital Suite Room 9 (Level 3) T4: Graph Exploration: Let me Show what is Relevant in 8:00AM ‑ 12:00PM your Graph ICC Capital Suite Room 12 (Level 3) T8: Online Evaluation for Effective Web Service 8:00AM ‑ 12:00PM Development ICC Capital Suite Room 6 (Level 3) T9: Redescription Mining: Theory, Algorithms, and 8:00AM ‑ 12:00PM Applications ICC Capital Suite Room 16 (Level 3) T11: Anti‑discrimination Learning: From Association to 8:00AM ‑ 12:00PM Causation ICC Capital Suite Room 1 (Level 3) T15: Graph Sketching, Sampling, Streaming, and 8:00AM ‑ 12:00PM Space‑Efficient Optimization ICC Capital Suite Room 15 (Level 3) 8:00AM ‑ 12:00PM T17: Artificial Intelligence
    [Show full text]
  • Partners for a New Beginning Status Report | January 2013 Table of Contents
    PARTNERS FOR A NEW BEGINNING STATUS REPORT | JANUARY 2013 TABLE OF CONTENTS A Letter from the Chairs 2 Looking Ahead to 2013 4 Projects & Local Chapter Overviews 6 Algeria 6 Egypt 8 Indonesia 10 Jordan 12 Mauritania 15 Morocco 16 Pakistan 18 Palestinian Territory 22 Tunisia 27 Turkey 29 Maghreb 32 Global 34 What We’re Doing 36 2012 Convenings and Partner Events 36 2012 Delegations 42 Upcoming in 2013 43 PNB Leadership 44 PNB Steering Committee 44 PNB-NAPEO Advisory Board 44 PNB Secretariat: Roles and Responsibilities 45 PNB Timeline(2010–2012) 46 PNB Statement of Commitment, Clinton Global Initiative, September 2010 47 PARTNERS FOR A NEW BEGINNING STATUS REPORT | JANUARY 2013 As Partners for a New Beginning (PNB) concludes its second year, we would like to take a moment to reflect on our collaborative work and take stock of our achievements. Our goal is to capitalize upon the momentum and success of this past year, to secure tangible results as we continue moving forward. A LETTER PNB can look proudly on the accomplishments of the last 12 months. They have contributed significantly to our mission. Our launch last year of local chapters in Jordan and Mauritania FROM THE CHAIRS attests to the sustained efforts of our partners abroad, the invaluable leadership of our Steering Committee, the instrumental support from the PNB Secretariat, and our partnership with the U.S. Department of State, codified recently in a renewed Memorandum of Understanding. With the help of its partners, PNB has launched, expanded, or pledged support for more than 180 projects since the partnership began in September 2010.
    [Show full text]