PDF-Learning from Labeled and Unlabeled Data on a Directed Graph Dengyong Zhou dengyong

zhoutuebingenmpgde Max Planck Institute for Biological Cybernetics Spemannstr 38 72076 Tubingen Germany Jiayuan Huang j9huangcsuwaterlooca School of Computer Science University of Waterloo Waterloo ON N2L 3G1 Canada Bernhard Scholkopf bernhardschoelk. John Blitzer. Shai Ben-David, Koby Crammer, Mark Dredze, Ryan McDonald, Fernando Pereira. Joint work with. Statistical models, multiple domains. Different Domains of Text. Huge variation in vocabulary & style. . Character Positioning. Christine Talbot. Character Positioning. Discovery News – Avatar: Motion Capture Mirrors Emotions . http. ://. news.discovery.com. /videos/avatar-making-the-movie/. MindMakers. A teacher who . needs. to know about . self-directed learning!. A teacher who knows about . self-directed learning!. Resourcing and Facilitating Self-directed Learning. How to work . SMARTER. not HARDER. and Semi-Supervised Learning. Longin Jan Latecki. Based on :. Xiaojin. Zhu. Semi-Supervised Learning with Graphs. PhD thesis. CMU-LTI-05-192, May 2005. Page, Lawrence and . Brin. , Sergey and . Motwani. Naman Agarwal. Michael Nute. May 1, 2013. Latent Variables. Contents. Definition & Example of Latent Variables. EM Algorithm Refresher. Structured SVM with Latent Variables. Learning under semi-supervision or indirect supervision. Semi-Supervised Classification Too. Zhu, Rogers, . Qian. , . Kalish. Presented by Syeda Selina Akter. Real World Situations. Do humans use unlabeled data in addition to labeled data?. Can this behavior be explained by mathematical models for Semi-supervised Machine Learning?. Directed Mixed Graph Models. Ricardo Silva. Statistical Science/CSML, University . College London. ricardo@stats.ucl.ac.uk. Networks: Processes and Causality, Menorca 2012. Graphical Models. Graphs provide a language for describing independence constraints. Grigory. . Yaroslavtsev. . Penn State + AT&T Labs - Research (intern). Joint work with . Berman (PSU). , . Bhattacharyya (MIT). , . Makarychev. (IBM). , . Raskhodnikova. (PSU). Directed. Spanner Problem. Introduction. Labelled data. Unlabeled data. cat. dog. (Image of cats and dogs without labeling). Introduction. Supervised learning: . E.g. . : image, . : class. . labels. Semi-supervised learning: . Energy Efficient AS for AF MIMO Two-way. IEEE ICC 2015 . 1. Is there a promising way? . Energy Efficient relay Antenna selection . For AF MIMO Two-way relay channels. Xingyu. Zhou. Bo . Bai. Wei Chen. Tamara Berg. CS 590-133 Artificial Intelligence. Many slides throughout the course adapted from Svetlana . Lazebnik. , Dan Klein, Stuart Russell, Andrew Moore, Percy Liang, Luke . Zettlemoyer. , Rob . Education UniversitédeMontréal&TraumaStudiesCentreMontréal,QuébecPostdoctoralFellow2014-2016–Examinationofgendervariationsinworkplaceaggressionandviolence–Supervisors:Dr.AlainMarcha Using Adult Education Strategies to Actively Cope with Chronic Illness. By Dr. Kristin . Brittain. & Dr. Valerie Bryan. Introduction. Due to the complexity of the health care system, patients are increasingly being asked to take more responsibility for their own self-care. . JFK. BOS. MIA. ORD. LAX. DFW. SFO. Presentation for use with the textbook, . Algorithm Design and Applications. , by M. T. Goodrich and R. Tamassia, Wiley, 2015. Directed Graphs. 2. Digraphs. A . digraph.