Machine Learning List: Vol. 16, No. 9 Monday, May 31, 2004 Contents Calls for Papers/Participation CFP: NeuroBotics - Bioinspired computation for Robotics CFP: First International Workshop on Knowledge Discovery CFP: CCS Workshop on Computer Security CFP - Special Track at 16th ICTAI-2004 CFP: Learning and Adaptation in Games at CGAIDE 2004 CFP: Special Issue on "Learning to Improve Reasoning" Career Opportunities Research Position at the NASA Jet Propulsion Laboratory job at Everest Tech The Machine Learning List is moderated. Contributions should be relevant to the scientific study of machine learning. Please send submissions for distribution to: ml@isle.org. For requests to be added, removed, or to change your email address, send email to: ml-request@isle.org. To keep mailings to a manageable size, please keep submissions brief. For meeting announcements, do highlight the meeting Web site and the goals of the event but omit information such as the program committee and talk schedules. Also, only first calls for papers/participation and brief change of deadline announcements will be included. The ML List moderator reserves the right to omit/edit submissions to meet these criteria. ---------------------------------------------------------------------- From: Stefan Wermter Subject: CFP: NeuroBotics - Bioinspired computation for Robotics Date: Fri, 14 May 2004 11:05:57 +0100 International Workshop on NeuroBotics: Bioinspired Computation for Robotics 20 September 2004 Call for Papers Substantial progress has been made recently in bio-inspired computation and robotics. This international workshop invites contributions to robotics which use methods of learning or artificial neural networks and/or are inspired by observations and results in neuroscience, cognitive science and animal behaviour. Topics of interest include but are not restricted to: Neural networks for robots Biomimetic robots Learning for robotics Cognitive robots Speech interfaces and neural networks Neural Vision Talking robots Spiking neural networks in robots Learning self localization and mapping Imitation and neural networks Cognitive Development in robots Location The workshop is organised as part of the 27th Conference on Artificial Intelligence (KI-2004) and runs parallel to the 34th Annual Meeting of the German Computer Science Society (Informatik 2004). The workshop will take place on September 20, 2004 at the University of Ulm, Germany. Format We invite contributions for half-hour talks and, if possible, additional short demonstrations. If we receive sufficient submissions of high quality, we plan to publish the revised articles of the workshop contributions in a journal or book. Deadlines Please send either abstracts of one page or full papers up to 8 pages (and if appropriate, possible descriptions of demonstrations) until June 9, 2004 to the organisers email address below. You will receive notification of acceptance, a more detailed workshop program and regarding the planned publication by July 5, 2004. Workshop registration: until August 20, 2004. Conference fees: will be not more than 100 Euro for the workshop. Registration at the conference will be optional. Organisation and more details: More details about the AI conference can be found at: http://ki2004.uni-ulm.de and more details about the organsing MirrorBot project to found at http://www.his.sunderland.ac.uk/mirrorbot/ and updates on this call at http://www.his.sunderland.ac.uk/mirrorbot/call.html ------------------------------ From: jgama Subject: CFP: First International Workshop on Knowledge Discovery Date: Tue, 18 May 2004 14:41:43 +0100 2nd Announcement & Call for Papers First International Workshop on Knowledge Discovery in Data Streams 24 September 2004, Pisa, Italy http://www.lsi.us.es/~aguilar/ecml2004/ Submission deadline: June 14, 2004 in conjunction with ECML/PKDD 2004: The 15th European Conference on Machine Learning (ECML) and The 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), http://ecmlpkdd.isti.cnr.it/ MOTIVATION Databases are growing incessantly and many sources produce data continuously. In many cases, we need to extract some sort of knowledge from this continuous stream of data. Examples include customer click streams, telephone records, large sets of web pages, multimedia data, scientific data, and sets of retail chain transactions. These sources are called data streams. The goal of this workshop is to convene researchers who deal with decision rules, decision trees, association rules, clustering, filtering, preprocessing, post processing, feature selection, visualization techniques, etc. from data streams and related themes. We are looking for all possible contributions related to inductive learning from data streams. The goal of this workshop is to convene researchers who deal with decision rules, decision trees, association rules, clustering, filtering,preprocessing, post processing, feature selection, visualization techniques, etc. from data streams and related themes. Research works presenting theoretical results, basic research, perspective solutions and practical developments are welcome, provided that they address the topic of the workshop. Position papers are also welcome and encouraged. Topics of Interest Topics include (but are not restricted to): * Data Stream Models * Clustering from Data Streams * Decision Trees from Data Streams * Association Rules from Data Streams * Decision Rules from Data Streams * Feature Selection from Data Streams * Visualization Techniques for Data Streams * Incremental on-line Learning Algorithms * Mining spatio-temporal data streams * Scalable Algorithms * Real-Time Applications * Real-World Applications Important Dates Submission deadline: June 14, 2004 Notification of acceptance: July 5, 2004 Camera-ready copies due: July 12, 2004 ------------------------------ From: Philip Chan Subject: CFP: CCS Workshop on Computer Security Date: Fri, 14 May 2004 22:08:54 -0400 (EDT) CALL FOR PAPERS CCS Workshop on Visualization and Data Mining for Computer Security (Viz/DMSEC) October 29, 2004 Wahsington, DC, USA (George Mason University, Fairfax, VA, USA) Held in conjunction with the Eleventh ACM Conference on Computer and Communications Security (CCS) http://www.cs.fit.edu/~pkc/vizdmsec04/ Information about security on large and complex computer networks is high volume, heterogeneous, distributed, and dynamic over time. Of interest to this workshop are two complementary methods to process high-dimensional data into knowledge: visualization and data mining. Visualization represents high-dimension security data in 2D/3D graphics and animations intended to facilitate quick inferences for situational awareness and focusing of attention on potential security events. Data mining focuses on algorithms to accurately detect patterns in high-dimension security data representing unauthorized system access or computer network attacks. Papers with demonstrated results will be given priority. Information on last year's DMSEC workshop can be found at http://www.cs.fit.edu/~pkc/dmsec03/ . Important Dates: Paper submissions due: June 18, 2004 Notifications to the authors: August 6, 2004 ------------------------------ From: "Ian Davidson" Subject: CFP - Special Track at 16th ICTAI-2004 Date: Tue, 25 May 2004 13:17:02 -0400 Learning at the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2004) Call for Papers Theories and Applications of Unsupervised and Semi-Supervised Learning Web site: http://www.cse.fau.edu/~zhong/cfp-learning.htm A Special Track at the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2004) November 15-17, 2004, Marriot Hotel, Boca Raton, Florida ICTAI 2004 Conference Web Site: http://www.cse.fau.edu/~ictai04/ Modern data mining and machine learning applications often involve learning from large amounts of data without output (i.e., category labels) or with only a very limited number of labels. Examples include automatic categorization of document collections, gene function analysis from gene expression (DNA microarray) data, hyper-spectral image segmentation/classification, and content-based image retrieval, etc. In these applications, category labels are usually difficult or expensive to get, or even dynamic. Traditional classification techniques have become insufficient in addressing these challenges; Various unsupervised clustering and semi-supervised learning algorithms have recently been proposed and successfully employed. The success of unsupervised and semi-supervised learning motivates further enhancements to existing algorithms and proposing new algorithms to cope with the requirements of real world problems. While typical applications have focused on clustering and classification tasks, there is a spectrum of possible learning situations such as: * learning from completely unlabeled data, learning from * unlabeled data and both positively and negatively labeled * data, learning from unlabeled and only positively (or only * negatively) labeled data, learning from partially * incorrectly labeled data and unlabeled data. This special track solicits and welcomes papers in the general area of applications of unsupervised and semi-supervised learning as well as algorithmic enhancements to handle issues raised in real world problems. Topics of interest (include but are not limited to) * Clustering with constraints Applications of clustering in * AI Modeling of learning with partial labels Semi-supervised * learning methods and applications Theoretical or empirical * evaluation of the value of labeled and unlabeled data * Comparative study of existing unsupervised and * semi-supervised learning methods Any related applications * (text clustering/classification, CBIR, scientific data * analysis, data mining for intrusion detection, ......) Key dates June 18, 2004: Deadline for paper submission August 2, 2004: Notification of acceptance September 3, 2004: Camera-ready papers due Any questions regarding this special track or the submission procedure, please don't hesitate to contact the track co-chairs at davidson@cs.albany.edu or zhong@cse.fau.edu. ------------------------------ From: P.Spronck@CS.unimaas.nl Subject: CFP: Learning and Adaptation in Games at CGAIDE 2004 Date: Mon, 24 May 2004 15:55:01 +0200 CALL FOR PAPERS Special Track on Learning and Adaptation in Games at the International Conference on Computer Games: Artificial Intelligence, Design and Education (CGAIDE) 2004 8-10 November 2004 Microsoft Campus, Reading, UK Artificial intelligence in computer games covers the behaviour and decision-making process of game-playing opponents. In classic analytical games, such as chess, checkers and go, the strongest game-playing programs rely mostly on fast search techniques, whereas in commercial games, such as action games, role-playing games and strategy games, the behaviour of opponents is commonly implemented as simple rule-based systems. With a few exceptions machine-learning techniques are rarely applied to state-of-the-art computer game playing systems. Machine-learning techniques may provide game-playing programs with the ability to improve their performance by learning from mistakes and successes, to automatically adapt to the strengths and weaknesses of a human player, to learn from their opponents by imitating their tactics, or to discover new knowledge by analysing game collections or perfect move databases. There is a relatively small group of enthusiastic researchers that investigate the use of machine-learning techniques to enhance computer games. Our aim is to bring them together at the CGAIDE 2004 conference, by having a special track on "Learning and Adaptation in Games", with a good selection of high quality papers in this research area. We also strive to use this track to increase the computer-games industry's awareness of machine-learning techniques. Topics of interest: The special track on "Learning and Adaptation in Games" will cover the application of machine-learning techniques to all aspects of computer games. The track is limited neither to specific types of games, nor to specific machine-learning techniques. Submissions: Draft paper submission : 30 July 2004 Notification of acceptance : 23 August 2004 Camera-ready submission deadline : 13 September 2004 Accepted papers will be published in the conference proceedings. Authors of the best of the accepted papers will be invited to publish their papers in the on-line International Journal of Intelligent Games and Simulation (http://www.scit.wlv.ac.uk/~cm1822/ijigs.htm). Detailed information on submitting papers is found at the CGAIDE website at http://www.scit.wlv.ac.uk/~cm1822/cgaide.htm. Submissions for the special track on "Learning and Adaptation in Games" can be sent either to the conference administrator (t.kaur2@wlv.ac.uk), or directly to the track organisers (p.spronck@cs.unimaas.nl). If you have questions regarding the special track, don't hesitate to contact the organisers. Organisers: Pieter Spronck Institute for Knowledge and Agent Technology, University of Maastricht p.spronck@cs.unimaas.nl Johannes Fuernkranz Knowledge Engineering Group, TU Darmstadt fuernkranz@informatik.tu-darmstadt.de ------------------------------ From: Seth Rogers Subject: CFP: Special Issue on "Learning to Improve Reasoning" Date: Fri, 21 May 2004 08:44:06 -0700 Special Issue of Computational Intelligence on "Learning to Improve Reasoning" Co-editors: Seth Rogers (srogers@csli.stanford.edu) Afzal Upal (afzal@eecs.utoledo.edu) Machine Learning has made great strides in recent years in maturing as a cohesive research topic and producing real-world applications, but most of the progress has been in the sub-topics of classification and reactive control. However, machine learning also aims to contribute in more complex tasks that involve reasoning, planning and inference. There has been a resurgence of interest in this area, as evidenced by the successful symposium on "reasoning and learning in cognitive systems" held at Stanford on March 21-22, 2004. The special issue will gather a sampling of recent research on machine learning for complex, multi-step performance tasks and present a comprehensive picture of the field. The issue would contain fresh approaches to a variety of specific tasks that are not already covered in the archival literature. Taken as a whole, this will inspire researchers to renew efforts to study learning on these more complex tasks and extend the capabilities within the current reach of machine learning systems. Research Areas: We welcome original and previously unpublished papers (previous publication of partial results at a conference/workshop is allowed) that make substantial contributions to the area of learning for multi-step performance tasks (such as reasoning, planning, and inference). These include papers that (a) propose novel algorithms for learning to improve the performance of domain independent reasoning systems, (b) review, compare, and analyze different learning paradigms providing new insights such as relating domain features to solution features, (c) advance evaluation techniques for measuring the performance of learning-for-reasoning systems, and (d) analyze issues involved in deploying the learning for reasoning systems in real world applications. The topics of particular interest include, but are not limited to, the following: - Machine learning for planning - Machine learning for problem solving - Machine learning for constraint programming - Machine learning for computer games - Speed-up learning - Learning to improve solution quality Review Criteria: All papers will be reviewed by at least two experts. An ideal paper will clearly define the learning problem, describe the proposed learning algorithm in enough detail to allow replication by others, specify and motivate the performance measure(s), and detail evidence that supports conclusions drawn by the authors. All submissions should be clearly written and must discuss relationship of the proposed research to previously published work. Editors reserve the right to return a submission without review if it is deemed not to address issues of interest identified in this CFP or adhere to the formatting guidelines. Formatting Guidelines: Manuscripts should conform to the formatting instructions found at . Due to the short timeline we will not be able to review submissions that are more than 30 pages long. Proposed Timeline: August 1, 2004: Letter of intent to submit sent to srogers@csli.stanford.edu September 1, 2004: full paper submission deadline March 1, 2005: decision August 26, 2005: publication Please contact srogers@csli.stanford.edu or afzal@eecs.utoledo.edu for further information. ------------------------------ From: Kiri Wagstaff Subject: Research Position at the NASA Jet Propulsion Laboratory Date: Thu, 6 May 2004 17:42:59 -0700 (PDT) The Machine Learning Systems group at the NASA Jet Propulsion Laboratory is seeking candidates for the following position: Machine Learning Researcher Requisition #: 1542 REQUIRES: B.S. degree in Physical or Computational Science with 5-10 years experience or M.S. degree in similar discipline with 3-8 years experience or equivalent directly related experience. Demonstrated experience in machine learning, pattern recognition techniques, and image analysis. Ability to conduct independent research, as demonstrated by peer-reviewed publications in professional journals and conferences. Ability to work effectively in a small team environment and to head and advise other members of the team in an area of expertise. Excellent verbal and written communication skills. Excellent programming and analytical skills. DESIRE: Ph.D. degree in Physical or Computational Science with 1-6 years experience or equivalent directly related experience. Experience with at least three of the following: kernel methods, time series analysis, pattern recognition, data mining, data fusion, supervised and unsupervised machine learning, Bayesian inference and parameter estimation, and reinforcement learning. Active professional in the Machine Learning research community. Willingness to work on diverse application areas with a variety of problem solving techniques. WILL: Perform collaborative scientific research and software engineering in the Machine Learning Systems Group of the Exploration Systems Autonomy Section (367). Will work both independently and as a leader in a team setting on R&D efforts to advance the state of the art in software to pursue the goal of enhanced autonomy and science return from space missions. Will lead and manage independent research tasks and work, collaborate on research tasks, and provide technical leadership. Will obtain funding to lead and conduct research and development by initiating and collaborating on proposals. Will present results at professional meetings and publish work in peer-reviewed journals. Applicants are invited to submit a CV, brief statement of research interests, and a list of publications to: Rebecca Castano rebecca.castano@jpl.nasa.gov Jet Propulsion Laboratory, MS 126-347 tel: (818) 393-5344 Pasadena, CA 91109, USA fax: (818) 393-5244 ------------------------------ From: "Anil Kamath" Subject: job at Everest Tech Date: Sun, 9 May 2004 08:20:33 -0700 Everest Technologies is a start-up with a unique, patent-pending technology to manage large complex search marketing campaigns. Our technology is based on sophisticated mathematical modeling and stochastic optimization techniques. We deliver search marketing services to some of the world's biggest online advertisers. The company was started by Stanford graduates and has many Stanford/Berkeley grads on its team. We are looking for an Algorithms Engineer with a strong background in machine learning algorithms to join our team and work on large scale complex problems in the area of search marketing. The Algorithms Engineer will work on developing statistical models and algorithms for real-time stochastic optimization on large scale numerical data. Candidates should have a Masters/PhD in Computer Science or a closely related discipline. Programming expertise and knowledge of machine learning, data mining, randomized algorithms or statistics are also required. Knowledge of working with large data sets, computer networks and distributed systems is extremely helpful. We are also looking for smart developers/interns for full/part-time positions. Please send resumes to jobs@everesttech.net ------------------------------ End of ML-LIST Digest Vol 16, No. 9 ************************************