Workshops		Neural Information Processing Systems: Natural and Synthetic
						The number of workshop proposals was particularly high this year. All together there will be seventeen NIPS*2002 workshops, of which three will last for two days, for a total of twenty workshop-days: a new record. We anticipate a great year not just in the number of workshops and in their quality, but in attendance as well: projections indicate that the workshops may surpass the main conference in total number of participants.
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		Workshops Schedule				Where and What Time?
Which Day?		Two-Day Workshops: Fri, Dec 13 & Sat, Dec 14 Functional Neuroimaging Multi-Agent Learning Propagation on Cyclic Graphs One-Day Workshops: Fri, Dec 13 Adaptation/Plasticity and Coding Bioinformatics Independent Component Analysis Neuromorphic Engineering Spectral Methods Statistics for Computational Experiments Unreal Data One-Day Workshops: Sat, Dec 14 Learning Invariant Representations Learning Rankings Negative Results On Learning Kernels Quantum Neural Computing Thalamocortical Processing Universal Learning Algorithms				The NIPS*2002 Workshops will be held at the Westin in Whistler BC, Canada, on Fri Dec 13 and Sat Dec 14, with sessions at 7:30-10:00am and 4:00-7:00pm. Thursday, Dec. 12 6:30pm - 8:30pm Welcoming Reception and Registration Friday, Dec. 13 7:00am - 11:00am Registration 7:30am - 10:30am Workshop Sessions 4:00pm - 7:00pm Workshop Sessions Saturday, Dec. 14 7:30am - 10:30am Workshop Sessions 4:00pm - 7:00pm Workshop Sessions 7:30pm - 10:30pm Banquet and Wrap-up
		Two-day Workshops (Friday and Saturday)				Organizers
		Propagation Algorithms on Graphs with Cycles: Theory and Applications Inference on graphs with cycles (loopy graphs) has drawn much attention in recent years. The problem arises in various fields such as AI, error-correcting codes, statistical physics, and image processing. Although exact inference is often intractable, much progress has been made in solving the problem approximately with local propagation algorithms. The aim of the workshop is to provide an overview of recent developments in methods related to belief propagation. We also encourage discussion of open theoretical problems and new possibilities for applications.				Shiro Ikeda, Kyushu Institute of Technology, Fukuoka, Japan Toshiyuki Tanaka, Tokyo Metropolitan University, Tokyo, Japan Max Welling, University of Toronto, Toronto, Canada
		Computational Neuroimaging: Foundations, Concepts & Methods This workshop will concentrate on the foundations of neuroimaging, including the relation between neural firing and BOLD, fast fMRI, and diffusion methods. The first day includes speakers on new Methods for Multivariate analysis using fMRI especially as they relate to Neural Modeling (ICA, SVM, or other ML methods), which will slip into the next morning, with cognitive neuroscience talks involving Network and specific Neural Modeling approaches to cognitive function on day two.				Stephen J. Hanson, Rutgers University, Newark, NJ, USA Barak A. Pearlmutter, University of New Mexico, Albuquerque, NM, USA Stephen Strother, University of Minnesota, Minneapolis, MN, USA Lars Kai Hansen, Technical University of Denmark, Lyngby, Denmark Benjamin Martin Bly, Rutgers University, Newark, NJ, USA
		Multi-Agent Learning: Theory and Practice Machine learning in a multi-agent system, where learning agents interact with other agents that are also simultaneously learning, poses a radically different set of issues from those arising in normal single-agent learning in a stationary environment. This topic is poorly understood theoretically but seems ripe for progress by building upon many recent advances in RL and in Bayesian, game-theoretic, decision-theoretic, and evolutionary learning. At the same time, learning is increasingly vital in fielded applications of multi-agent systems. Many application domains are envisioned in which teams of software agents or robots learn to cooperate to achieve global objectives. Learning may also be essential in many non-cooperative domains such as economics and finance, where classical game-theoretic solutions are either infeasible or inappropriate. This workshop brings together researchers studying multi-agent learning from a variety of perspectives. Our invited speakers include leading AI theorists, applications developers in fields such as robotics and e-commerce, as well as social scientists studying learning in multi-player human-subject experiments. Slots are also available for contributed talks and/or posters.				Gerald Tesauro, IBM Research, NY, USA Michael L. Littman, Rutgers University, New Brunswick, NJ, USA
		One-day Workshops (Friday)				Organizers
		The Role of Adaptation/Plasticity in Neuronal Coding A ubiquitous characteristic of neuronal processing is the ability to adapt to an ever changing environment on a variety of different time scales. Although the different forms of adaptation/ plasticity have been studied for some time, their role in the encoding process is still not well understood. The most widely utilized measures assume time-invariant encoding dynamics even though mechanisms serving to modify coding properties are continually active in all but the most artificial laboratory conditions. Important questions include: (1) how do encoding dynamics and/or receptive field properties change with time and the statistics of the environment?, (2) what are the underlying sources of these changes?, (3) what are the resulting effects on information transmission and processing in the pathway?, and (4) can the mechanisms of plasticity/adaptation be understood from a behavioral perspective? It is the goal of this workshop to discuss neuronal coding within several different experimental paradigms, in order to explore these issues that have only recently been addressed in the literature.				Garrett B. Stanley, Harvard University, Cambridge, MA, USA Tai Sing Lee, Carnegie Mellon University, Pittsburgh, PA, USA
		Independent Component Analysis and Beyond Independent component analysis (ICA) aims at extracting unknown hidden factors/components from multivariate data using only the assumption that the unknown factors are mutually independent. Since the introduction of ICA concepts in the early 80s in the context of neural networks and array signal processing, many new successful algorithms have been proposed that are now well-established methods. Since then, diverse applications in telecommunications, biomedical data analysis, feature extraction, speech separation, time-series analysis and data mining have been reported. Notably of special interest for the NIPS community are, first, the application of ICA techniques to process multivariate data from various neuro-physiological recordings and second, the interesting conceptual parallels to information processing in the brain. Recently exciting developments have moved the field towards more general nonlinear or nonindependent source separation paradigms. The goal of the planed workshop is to bring together researchers from the different fields of signal processing, machine learning, statistics and applications to explore these new directions.				Stefan Harmeling, Fraunhofer FIRST, Berlin, Germany Luis Borges de Almeida, INESC ID, Lisbon, Portugal Erkki Oja, HUT, Helsinki, Finland Dinh-Tuan Pham, LMC-IMAG, Grenoble, France
		Spectral Methods in Dimensionality Reduction, Clustering, and Classification Data-driven learning by local or greedy parameter update algorithms is often a painfully slow process fraught with local minima. However, by formulating a learning task as an appropriate algebraic problem, globally optimal solutions may be computed efficiently in closed form via an eigendecomposition. Traditionally, this spectral approach was thought to be applicable only to learning problems with an essentially linear structure, such as principal component analysis or linear discriminant analysis. Recently, researchers in machine learning, statistics, and theoretical computer science have figured out how to cast a number of important nonlinear learning problems in terms amenable to spectral methods. These problems include nonlinear dimensionality reduction, nonparameteric clustering, and nonlinear classification with fully or partially labeled data. Spectral approaches to these problems offer the potential for dramatic improvements in efficiency, accuracy, optimality and reproducibility relative to traditional iterative or greedy learning algorithms. Furthermore, numerical methods for spectral computations are extremely mature and well understood, allowing learning algorithms to benefit from a long history of implementation efficiencies in other fields. The goal of this workshop is to bring together researchers working on spectral approaches across this broad range of problem areas, for a series of talks on state-of-the-art research and discussions of common themes and open questions.				Josh Tenenbaum, M.I.T., Cambridge, MA, USA Sam Roweis, University of Toronto, Ontario, Canada
		Neuromorphic Engineering in the Commercial World We propose a one-day workshop to discuss strategies, opportunities and success stories in the commercialization of neuromorphic systems. Towards this end, we will be inviting speakers from industry and universities with relevant experience in the field. The discussion will cover a broad range of topics, from visual and auditory processing to olfaction and locomotion, focusing specifically on the key elements and ideas for successfully transitioning from neuroscience to commercialization.				Timothy Horiuchi, University of Maryland, College Park, MD, USA Giacomo Indiveri, University-ETH Zurich, Zurich, Switzerland Ralph Etienne-Cummings, University of Maryland, College Park, MD, USA
		Statistical Methods for Computational Experiments in Visual Processing and Computer Vision In visual processing and computer vision, computational experiments play a critical role in explaining algorithm and system behavior. Disciplines such as psychophysics and medicine have a long history of designing experiments. Vision researchers are still learning how to use computational experiments to explain how systems behave in complex domains. This workshop will focus on new and better experiment experimental methods in the context of visual processing and computer vision.				Ross Beveridge, Colorado State University, Colorado, USA Bruce Draper, Colorado State University, Colorado, USA Geof Givens, Colorado State University, Colorado, USA Ross J. Micheals, NIST, Maryland, USA Jonathon Phillips, DARPA & NIST, Maryland, USA
		Unreal Data: Principles of Modeling Nonvectorial Data A large amount of research in machine learning is concerned with classification and regression for real-valued data which can easily be embedded into a Euclidean vector space. This is in stark contrast with many real world problems, where the data is often a highly structured combination of features, a sequence of symbols, a mixture of different modalities, may have missing variables, etc. To address the problem of learning from non-vectorial data, various methods have been proposed, such as embedding the structures in some metric spaces, the extraction and selection of features, proximity based approaches, parameter constraints in Graphical Models, Inductive Logic Programming, Decision Trees, etc. The goal of this workshop is twofold. Firstly, we hope to make the machine learning community aware of the problems arising from domains where non-vectorspace data abounds and to uncover the pitfalls of mapping such data into vector spaces. Secondly, we will try to find a more uniform structure governing methods for dealing with non-vectorial data and to understand what, if any, are the principles underlying the modeling of non-vectorial data.				Alexander J. Smola, Australian National Univ., Canberra, Australia Gunnar Raetsch, Australian National Univ., Canberra, Australia Zoubin Ghahramani, University College London, London, UK
		Machine Learning Techniques for Bioinformatics This workshop will cover the development and application of machine learning techniques in application to molecular biology. Contributed papers are welcome from any topic relevant to this theme including, but not limited to, analysis of expression data, promoter analysis, protein structure prediction, protein homology detection, detection of splice junctions, and phylogeny, for example. Contributions are most welcome which propose new algorithms or methods, rather than the use of existing techniques. In addition to contributed papers we expect to have several tutorials covering different areas where machine learning techniques are have been successfully applied in this domain.				Colin Campbell, University of Bristol, UK Phil Long, Genome Institute of Singapore
		One-day Workshops (Saturday)				Organizers
		Thalamocortical Processing in Audition and Vision All sensory information (except olfactory) passes through the thalamus before reaching the cortex. Are the principles governing this thalamocortical transformation shared across sensory modalities? This workshop will investigate this question in the context of audition and vision. Questions include: Do the LGN and MGN play analogous roles in the two sensory modalities? Are the cortical representations of sound and light analogous? Specifically, the idea is to talk about cortical processing (as opposed to purely thalamic), how receptive fields are put together in the cortex, and the implications of these ideas to the nature of information being encoded and extracted at the cortex.				Tony Zador, Cold Spring Harbor Lab., Cold Spring Harbor, NY, USA Shihab Shamma, University of Maryland, College Park, MD, USA
		Learning of Invariant Representations Much work in recent years has shown that the sensory coding strategies employed in the nervous systems of many animals is well matched to the statistics of their natural environment. For example, it has been shown that lateral inhibition occuring in the retina may be understood in terms of a decorrelation or `whitening' strategy (Srinivasan et al., 1982; Atick & Redlich, 1992), and that the receptive properties of cortical neurons may be understood in terms of sparse coding or ICA (Olshausen & Field, 1996; Bell & Sejnowski, 1997; van Hateren & van der Schaaf, 1998). However, most of these models do not address the question of which properties of the environment are interesting or relevant and which others are behaviourally insignificant. The purpose of this workshop is to focus on unsupervised learning models that attempt to represent features of the environment which are invariant or insensitive to variations such as position, size, or other factors.				Konrad Paul Koerding, ETH/UNI Zuerich, Switzerland Bruno. A. Olshausen, U.C. Davis & RNI, CA, USA
		Quantum Neural Computing Recently there has been a resurgence of interest in quantum computers because of their potential for being very much smaller and very much faster than classical computers, and because of their ability in principle to do hereofore impossible calculations, such as factorization of large numbers in polynomial time. We will explore ways to implement biologically inspired quantum computing in network topologies, thus exploiting both the intrinsic advantages of quantum computing and the adaptability of neural computing. This workshop will follow up on our very successful NIPS 2000 workshop and the IJCNN 2001 Special Session. Aspects/approaches to be explored will include: quantum hardware, e.g., SQUIDs, nmr, trapped ions, quantum dots, and molecular computing; theoretical and practical limits to quantum and quantum neural computing, e.g. noise, error correction, and decoherence; and simulations.				Elizabeth C. Behrman, Wichita State University, Wichita, KS, USA James E. Steck, Wichita State University, Wichita, KS, USA
		Universal Learning Algorithms and Optimal Search Recent theoretical and practical advances are currently driving a renaissance in the fields of Universal Learners (rooted in Solomonoff's universal induction scheme, 1964) and Optimal Search (rooted in Levin's universal search algorithm, 1973). Both are closely related to the theory of Kolmogorov complexity. The new millennium has brought several significant developments including: Sharp expected loss bounds for universal sequence predictors, theoretically optimal reinforcement learners for general computable environments, computable optimal predictions based on natural priors that take algorithm runtime into account, and practical, bias-optimal, incremental, universal search algorithms. Topics will also include: Practical but general MML/MDL/SRM approaches with theoretical foundation, weighted majority approaches, and no free lunch theorems.				Juergen Schmidhuber, IDSIA, Manno-Lugano, Switzerland Marcus Hutter, IDSIA, Manno-Lugano, Switzerland
		On Learning Kernels Recent theoretical advances and experimental results have drawn considerable attention to the use of kernel methods in learning systems. For the past five years, a growing community has been meeting at the NIPS workshops to discuss the latest progress in learning with kernels. Recent research in this area addresses the problem of learning the kernel itself from data. This subfield is becoming an active research area, offering a challenging interplay between statistics, advanced convex optimization and information geometry. It presents a number of interesting open problems. The workshop has two goals. First, it aims at discussing state-of-the-art research on 'learning the kernel', as well as giving an introduction to some of the new techniques used in this subfield. Second, it offers a meeting point for a diverse community of researchers working on kernel methods. As such, contributions from ALL subfields in kernel methods are welcome and will be considered for a poster presentation, with priority to very recent results. Furthermore, contributions on the main theme of learning kernels will be considered for oral presentations. Deadline for submissions: Nov 15, 2002.				Nello Cristianini, U.C. Davis, California, USA Tommi Jaakkola, M.I.T., Massachusetts, USA Michael I. Jordan, U.C. Berkeley, California, USA Gert R.G. Lanckriet, U.C. Berkeley, California, USA
		Negative Results and Open Problems In mathematics and theoretical computer science, exhibiting counter examples is part of the established scientific method to rule out wrong hypotheses. Yet, negative results and counter examples are seldom reported in experimental papers, although they can be very valuable. Our workshop will be a forum to freely discuss negative results and introduce the community to challenging open problems. This may include reporting experimental results of principled algorithms that obtain poor performance compared to seemingly dumb heuristics; experimental results that falsify an existing theory; counter examples to a generally admitted conjecture; failure to find a solution to a given problem after various attempts; and failure to demonstrate the advantage of a given method after various attempts. If you have interesting negative results (not inconclusive results) or challenging open problems, you may submit an abstract before November 15, 2002.				Isabelle Guyon, Clopinet, California, USA
		http://www.cs.cornell.edu/People/tj/ranklearn Beyond Classification and Regression: Learning Rankings, Preferences, Equality Predicates, and Other Structures Not all supervised learning problems fit the classification/ regression function-learning model. Some problems require predictions other than values or classes. For example, sometimes the magnitude of the values predicted for cases are not important, but the ordering these values induce is important. This workshop addresses supervised learning problems where either the goal of learning or the input to the learner is more complex than in classification and regression. Examples of such problems include learning partial or total orderings, learning equality or match rules, learning to optimize non-standard criteria such as Precision and Recall or ROC Area, using relative preferences as training examples, learning graphs and other structures, and problems that benefit from these approaches (e.g., text retrieval, medical decision making, protein matching). The goal of this one-day workshop is to discuss the current state-of-the-art, and to inspire research on new algorithms and problems. To submit an abstract, visit the webpage.				Rich Caruana, Cornell University, NY, USA Thorsten Joachims, Cornell University, NY, USA
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