/ Events

This is a special research workshop to mark the 250th anniversary of the death of Rev Thomas Bayes. Bayes was a student of the University of Edinburgh from 1719 to c. 1722. The theme of the workshop is all aspects of the research area that has come to be known as Bayesian statistics.

The workshop will be held from Mon 5 Sept to Wed 7 Sept, 2011.

Professor David Dunson (Duke University, North Carolina, USA) has kindly agreed to give a keynote lecture.

Also, Professor David Spiegelhalter FRS (University of Cambridge) will give a lecture on Bayesian methods aimed at the general public. The lecture will be held on the evening of Monday 5th September. All attending the workshop are invited to attend.

Organisers

In 2011 a Joint Conference of the German Classification Society (GfKl) and the German Association for Pattern Recognition (DAGM) will take place in Frankfurt am Main. The focus of this conference will be "Algorithms from & for Nature and Life" .

This conference will be preceded by a one-day Symposium of the International Federation of Classification Societies (IFCS-2011), which will feature keynote addresses by Gilles Celeux and Douglas Steinley, a President's invited session (with Bruce Lindsay as President's invited speaker), and a Presidential address by IFCS President Geoff McLachlan.

Our workshop focuses on the common space delimited by three main areas: machine learning, agent technologies and formal language theory. The main goal of the workshop is to promote interdisciplinarity among people working in such disciplines, boosting the interchange of knowledge and viewpoints between specialists. This interdisciplinary research can provide new models that may improve AI technologies.

Understanding human learning well enough to reproduce aspects of that learning behaviour in a computer system is a worthy scientific goal. One of the less understood learning capacities of humans is their ability to acquire a natural language. In order to better understand natural language acquisition,  research in formal models of language learning, within the field of machine learning, has received significant attention. The theory of formal language theory is central to the field of machine learning, since the specific subfield of grammatical inference deals with the process of learning grammars and languages from data.

The theory of formal languages was mainly originated from mathematics and generative linguistics as a tool for modelling and investigating syntax of natural languages, and then it played an important role in the field of computer science. While the first generation of formal languages was based on rewriting, a further development in this area has been the idea of several devices collaborating for achieving a common goal. Formal language theory has taken advantage of the idea of formalizing agent architectures where a hard task is distributed among several task-specific agents. In fact, non-standard formal language models have been proposed as grammatical models of agent systems.

So, the areas of machine learning, agent technologies and formal languages are clearly related. Therefore we are interested in contributions on any interaction between those three research areas.

Topics include (but are not limited to):

  • Agent systems modelling
  • Computational models of language learning
  • Theoretical aspects of Grammatical Inference
  • Formal models of bio-inspired agent systems
  • Theoretical descriptions of languages based on agent systems
  • Learning agents: Machine learning and Agent systems
  • Applications of machine learning and agent technologies to natural language processing, human-computer interaction and language evolution.
  • Intelligent human-computer interaction

 

There is a growing need and interest in analyzing data that is best represented as a graph, such as the World Wide Web, social networks, social media, biological networks, communication networks, and physical network systems. Traditionally, methods for mining and learning with such graphs has been studied independently in several research areas, including machine learning, statistics, data mining, information retrieval, natural language processing, computational biology, statistical physics, and sociology. However, we note that contributions developed in one area can, and should, impact work in the other areas and disciplines. One goal of this workshop is to foster this type of interdisciplinary exchange, by encouraging abstraction of the underlying problem (and solution) characteristics during presentation and discussion.

In particular, this workshop is intended to serve as a forum for exchanging ideas and methods, developing new common understandings of the problems at hand, sharing of data sets where applicable, and leveraging existing knowledge from different disciplines. The goal is to bring together researchers from the related disciplines, including academia, industry and government, and create a forum for discussing recent advances in analysis of graphs. In doing so we aim to better understand the overarching principles and the limitations of our current methods, and to inspire research on new algorithms and techniques for mining and learning with graphs.

Organization Committee

  • Kristian Kersting, Fraunhofer IAIS and University of Bonn
  • Prem Melville, IBM Research
  • Jennifer Neville, Purdue University
  • David Page, University of Wisconsin

Program Committee

  • Edoardo M. Airoldi, Harvard University
  • Mohammad Al Hasan, Indiana University-Purdue University Indianapolis
  • Aris Anagnostopoulos, Sapienza University of Rome
  • Arindam Banerjee, University of Minnesota
  • Christian Bauckhage, Fraunhofer IAIS
  • Francesco Bonchi, Yahoo! Research
  • Karsten Borgwardt, Max Planck Institute
  • Ulf Brefeld, Yahoo! Research
  • Diane Cook, Washington State University
  • Corinna Cortes, Google Research
  • Luc De Raedt, Katholieke Universiteit Leuven
  • Tina Eliassi-Rad, Rutgers University
  • Stephen Fienberg, Carnegie Melon University
  • Peter Flach, University of Bristol
  • Thomas Gartner, University of Bonn and
  • Fraunhofer IAIS
  • Brian Gallagher, Lawrence Livermore National Labs
  • Aris Gionis, Yahoo! Research
  • David Gleich, Sandia National Labs
  • Marco Gori, University of Siena
  • Marko Grobelnik, J. Stefan Institute
  • Jiawei Han, University of Illinois at Urbana-Champaign
  • Shawndra Hill, University of Pennsylvania
  • Larry Holder, Washington State University
  • Jake Hofman, Yahoo! Research
  • Manfred Jaeger, Aalborg University
  • Thorsten Joachims, Cornell University
  • Tamara Kolda, Sandia National Labs
  • Jure Leskovec, Stanford University
  • Bo Long, Yahoo! Research
  • Sofus Macskassy, Fetch Technologies
  • Dunja Mladenic, J. Stefan Institute
  • Srinivasan Parthasarathy, Ohio State University
  • Volker Tresp, Siemens CT
  • Chris Volinsky, AT&T Labs Research
  • Stefan Wrobel, University of Bonn and Fraunhofer IAIS
  • Xifeng Yan, University of California at Santa Barbara
  • Philip Yu, University of Illinois at Chicago
  • Mohammed Zaki, Rensselaer Polytechnic Institute
  • Zhongfei (Mark) Zhang, Binghamton University

The Fourth Workshop on Information Theoretic Methods in Science and Engineering (WITMSE) will be held in Helsinki, Finland, on August 7–10, 2011. The workshop will cover hot topics at the intersection of statistics, information theory, machine learning, and their applications. The technical program will include plenary lectures and invited talks.

Programme co-chairs:

 

MLSB11, the Fifth International Workshop on Machine Learning in Systems Biology will be held in Vienna, Austria on July 20-21, 2011.

The aim of this workshop is to contribute to the cross-fertilization between the research in machine learning methods and their applications to systems biology (i.e., complex biological and medical questions) by bringing together method developers and experimentalists. We encourage submissions bringing forward methods for discovering complex structures (e.g. interaction networks, molecule structures) and methods supporting genome-wide data analysis.

The Workshop is organized as "Satellite Meeting" of the 19th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and 10th European Conference on Computational Biology (ECCB).

Motivation

Molecular biology and all the biomedical sciences are undergoing a true revolution as a result of the emergence and growing impact of a series of new disciplines/tools sharing the "-omics" suffix in their name. These include in particular genomics, transcriptomics, proteomics and metabolomics, devoted respectively to the examination of the entire systems of genes, transcripts, proteins and metabolites present in a given cell or tissue type.
The availability of these new, highly effective tools for biological exploration is dramatically changing the way one performs research in at least two respects. First, the amount of available experimental data is not a limiting factor any more; on the contrary, there is a plethora of it. Given the research question, the challenge has shifted towards identifying the relevant pieces of information and making sense out of it (a "data mining" issue). Second, rather than focus on components in isolation, we can now try to understand how biological systems behave as a result of the integration and interaction between the individual components that one can now monitor simultaneously (so called "systems biology").

Taking advantage of this wealth of "genomic" information has become a conditio sine qua non for whoever ambitions to remain competitive in molecular biology and in the biomedical sciences in general. Machine learning naturally appears as one of the main drivers of progress in this context, where most of the targets of interest deal with complex structured objects: sequences, 2D and 3D structures or interaction networks. At the same time bioinformatics and systems biology have already induced significant new developments of general interest in machine learning, for example in the context of learning with structured data, graph inference, semi-supervised learning, system identification, and novel combinations of optimization and learning algorithms.

Molecular biology and all the biomedical sciences are undergoing a true revolution as a result of the emergence and growing impact of a series of new disciplines/tools sharing the “-omics” suffix in their name. These include in particular genomics, transcriptomics, proteomics and metabolomics, devoted respectively to the examination of the entire systems of genes, transcripts, proteins and metabolites present in a given cell or tissue type.

 

Textual inference and paraphrase have attracted a significant amount of attention in recent years. Many NLP tasks, including question answering, information extraction, and text summarization, can be mapped at least partially onto the recognition of textual entailments and the detection of semantic equivalence between texts. Robust and accurate algorithms and resources for inference and paraphrasing can therefore be beneficial for a broad range of NLP applications, and have generally stimulated research in the area of applied semantics.

TextInfer 2011 invites both theoretical and applied research contributions on the topic of inference. We aim to bring together empirical approaches (which have tended to dominate textual entailment events) with formal approaches to inference (which are more often presented at events like ICoS or IWCS). We feel that the time is ripe for researchers from both groups to join for this event, with the goal of establishing a discussion on how the two approaches relate to one another, and how to define interfaces between the two methodologies.

Chairs

  • Sebastian Pado, Heidelberg University
  • Stefan Thater, Saarland University

Organizing Committe

  • Peter Clark, Vulcan Inc.
  • Ido Dagan, Bar-Ilan University
  • Katrin Erk, University of Texas at Austin
  • Fabio Massimo Zanzotto, University of Rome "Tor Vergata"

Program Committee

  • Richard Bergmair, University of Cambridge
  • Johan Bos, University of Groningen
  • Aljoscha Burchardt, DFKI GmbH
  • Chris Callison-Burch, John Hopkins University
  • Phillip Cimiano, Bielefeld University
  • David Clausen, Stanford University
  • Ann Copestake, Cambridge University
  • Kees van Deemter, Aberdeen University
  • Bill Dolan, Microsoft Research
  • Mark Dras, Macquarie University
  • Markus Egg, HU Berlin
  • Anette Frank, Heidelberg University
  • Claire Gardent, LORIA
  • Andy Hickl, Extractiv/Swingly
  • Graeme Hirst, University of Toronto
  • Jerry Hobbs, USC/ISI
  • Kentaro Inui Tohoku University
  • Hans-Ulrich Krieger, DFKI
  • Piroska Lendvai, Hungarian Academy of Sciences
  • Bill MacCartney, Google
  • Bernardo Magnini, Fondazione Bruno Kessler
  • Marie-Catherine de Marneffe, Stanford University
  • Erwin Marsi, NTNU
  • Yashar Mehdad, FBK
  • Detmar Meurers, Tuebingen University
  • Shachar Mirkin, Bar-Ilan University
  • Michael Mohler, University of North Texas
  • Dan Moldovan, University of Texas at Dallas
  • Robero Navigli, University of Rome
  • Patrick Pantel, Microsoft Research
  • Marco Pennacchiotti, Yahoo!
  • Ian Pratt-Hartmann, Manchester University
  • Dan Roth, University of Illinois at Urbana-Champaign
  • Satoshi Sato, Nagoya University
  • Satoshi Sekine, New York University
  • Idan Szpektor, Yahoo!
  • Ivan Titov, Saarland University
  • Antonio Toral, Dublin City University
  • Kentaro Torisawa, NICT
  • Annie Zaenen, PARC

There is an increasing interest in social web mining, as we can see from the ACM workshop on Social Web Search and Analysis. It is not until recently that great progresses have been made in mining social network for various applications, e.g., making personalized recommendations. This workshop focuses on the study of diverse aspects of social networks with their applications in domains including mobile recommendations, service providers, electronic commerce, etc.

Social networks have actually played an important role in different domains for about a decade, particularly in recommender systems. In general, traditional collaborative filtering approaches can be considered as making personalized recommendations based on implicit social interaction, where social connections are defined by some similarity metrics on common rated items, e.g., movies for the Netflix Prize.

With the recent development of Web 2.0, there emerges a number of globally deployed applications for explicit social interactions, such as Facebook, Flickr, LinkedIn, Twitter, etc. These applications have been exploited by academic institutions and industries to build modern recommender systems based on social networks, e.g., Microsoft's Project Emporia that recommends tweets to user based on their behaviors.

In recent years, rapid progress has been made in the study of social networks for diverse applications. For instance, researchers have proposed various tensor factorization techniques to analyze user-item-tag data in Flickr for group recommendations. Also, researchers study Facebook to infer users' preferences.

However, there exist many challenges in mining social web and its application in recommender systems. Some are:

  • What is the topology of social networks for some specific application like LinkedIn?
  • How could one build optimal models for social networks such as Facebook?
  • How can one handle the privacy issue caused by utilizing social interactions for making recommendation?
  • How could one model a user's preferences based on his/her social interactions?

We hope to gather scientific researchers and industry in order to discuss the challenges, exchange ideas, and promote collaborations across different groups.

Program Co-Chair

Program Committee

  • Jean-Marc Andreoli, Xerox Research Centre Europe, France
  • Cedric Archambeau, Xerox Research Centre Europe, France
  • Guillaume Bouchard, Xerox Research Centre Europe, France
  • Tiberio Caetano, NICTA - ANU, Australia
  • Wei Chen, Microsoft Research Asia, China
  • Boris Chidlovskii, Xerox Research Centre Europe, Grenoble, France
  • Peter Christen, Australian National University, Australia
  • Nello Cristianini, University Of Bristol, UK
  • Hakim Hacid, Alcatel-Lucent Bell Labs, France
  • Jian Huang, Google Pittsburgh, USA
  • Jure Leskovec, Stanford University, USA
  • Ernesto William De Luca, Technical University of Berlin - DAI-Labor, Germany
  • Sherif Sakr, NICTA - UNSW, Australia
  • Scott Sanner, NICTA - ANU, Australia
  • Markus Schedl, Johannes Kepler University Linz, Austria
  • Fabrizio Silvestri, ISTI CNR, Italy
  • Julia Stoyanovich, University of Pennsylvania, USA
  • Aixin Sun, Nanyang Technological University, Singapore
  • Paul Thomas, CSIRO, Australia
  • Antti Ukkonen, Yahoo! Research Barcelona, Spain
  • Jie (Jessie) Yin, CSIRO, Australia
  • Yi Zhang, University of California, Santa Cruz, USA
  • Onno Zoeter, Xerox Research Centre Europe, Grenoble, France

 

Motivation

We are organizing a workshop of gesture and sign language recognition from video data and still images. Gestures can originate from any body motion or state but commonly originate from the face or hand. Much recent research has focused on emotion recognition from face and hand gestures, with applications in gaming, marketing, and computer interfaces. Many approaches have been made using cameras and computer vision algorithms to interpret sign language for the deaf. However, the identification and recognition of postures and human behaviors is also the subject of gesture recognition techniques and has gained importance in applications such as video surveillance. This workshop aims at gathering researchers from different application domains working on gesture recognition to share algorithms and techniques.

Benchmark

One of the goals of the workshop is to launch a benchmark program on gesture recognition. Part of the workshop will be devoted to discussing the modalities of the benchmark. See our website under construction. Download sample data [or get your sample data DVD at the workshop]. Participate in a data exchange [You will need first to register to our Google group gesturechallenge to get access to the data exchange website].

Participation

We invite contributions relevant to gesture recognition, including:

Algorithms for gesture and activity recognition, in particular addressing:

  • Learning from unlabeled or partially labeled data
  • Learning from few examples per class, and transfer learning.
  • Continuous gesture recognition and segmentation
  • Deep learning architectures, including convolutional neural networks
  • Gesture recognition in challenging scenes, including cluttered/moving backgrounds or moving cameras, or scenes where multiple persons are present.
  • Integrating information from multiple channels (e.g., position/motion of multiple body parts, hand shape, facial expressions).

Data representations
Applications pertinent to the workshop topic, such as involving:

  • Video surveillance
  • Image or video indexing and retrieval
  • Recognition of sign languages for the deaf
  • Emotion recognition and affective computing
  • Computer interfaces
  • Virtual reality
  • Robotics
  • Ambiant intelligence
  • Games
  • Datasets and benchmarks

Program committee

  • Aleix Martinez, Ohio State University, USA
    David W. Aha, Naval Research Laboratory, USA
  • Abe Schneider, Knexus Research, USA
    Jeffrey Cohn, Carnegie Mellon University, USA
  • Martial Hebert, Carnegie Mellon University, USA
  • Dimitris Metaxas, Rutgers,  New Jersey, USA
  • Christian Vogler, ILSP Athens, Greece
  • Sudeep Sarkar, University of South Florida, USA
  • Graham Taylor, NYU, New-York, USA
  • Andrew Ng, Stanford Univ., Palo Alto, CA, USA
  • Andrew Saxe, Stanford Univ., Palo Alto, CA, USA
  • Quoc Le, Stanford Univ., Palo Alto, CA, USA
  • David Forsyth, University of Illinois at Urbana-Champaign, USA
  • Maja Pantic, Imperial College, London
  • Philippe Dreuw, RWTH Aachen University, Germany
  • Richard Bowden, Univ. Surrey, UK
    Fernando de la Torre, Carnegie Mellon University, USA
  • Paulo Gotardo, Ohio State University, Ohio, USA
  • Carol Neidle, Boston University, MA, USA
  • Trevor Darrell, UC Berkeley/ICSI, Berkeley, California, USA
  • Greg Mori, Simon Fraser University, Canada
    Matthew Turk, UC Santa Barbara, USA
  • Atiqur Rahman Ahad, Faculty of Engineering, Kyushu Institute of Technology, Japan
  • Mingyu Chen, Georgia Institute of Technology, USA
  • Wenhui Xu, Georgia Institute of Technology, USA
  • Jesus-Omar Ocegueda-Gonzalez, University of Houston, USA
  • Thomas Kinsman, Rochester Institute of Technology, USA
  • András Lőrincz,  Eötvös Loránd University, Budapest, Hungary
  • Upal Mahbub, Bangladesh University of Engineering, Bangladesh
  • Subhransu Maj, UC Berkeley, CA, USA
  • Lubomir Bourdev,  UC Berkeley, CA, USA
  • Vassilis Pitsikalis, NTUA, Greece

Organizing committee

  • Isabelle Guyon, Clopinet, Berkeley, California
  • Vassilis Athitsos, University of Texas at Arlington
  • Jitendra Malik, UC Berkeley, California
  • Ivan Laptev, INRIA, France

Following the successful U.K. meetings in 2009 (London), 2010 (Aston), 2011 (London), 2012 (London) and 2013 (London) on Statistical Mechanics of Glassy and Complex Systems, we are looking forward to holding the next meeting at King's College London.  Past meetings have covered: physics of structural glasses, spin glasses, poly-disperse systems, biological networks, econophysics, optimisation, machine learning, etc.

Logo

Follow us:

© Knowledge 4 All Foundation Ltd.