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作者： Anand, Sarabjot Singh Bunescu, Razvan Carvcdho, Vitor Chomicki, Jan Conitzer, Vincent Cox, Michael T. Dignum, Virginia Dodds, Zachary Dredze, Mark Furcy, David Gabrilovich, Evgeniy Göker, Mehmet H. Guesgen, Hans Hirsh, Haym Jannach, Dietmar Junker, Ulrich Ketter, Wolfgang Kobsa, Alfred Koenig, Sven Lau, Tessa Lewis, Lundy Matson, Eric Metzler, Ted Mihalcea, Rada Mobasher, Bamshad Pineau, Joelle Poupart, Pascal Raja, Anita Ruml, Wheeler Sadeh, Norman Shani, Guy Shapiro, Daniel Smith, Trey Taylor, Matthew E. Wagstaff, Kiri Walsh, William Zhou, Rong Department of Computer Science University of Warwick United Kingdom School of Electrical Engineering and Computer Science Ohio University United States Microsoft Live Labs United States computer science and engineering University at Buffalo United States Computer science and economics Duke University United States Intelligent Computing group of BBN Technologies Utrecht University Netherlands Department of Computer science Harvey Mudd College United States University of Pennsylvania United States University of Wisconsin United States Yahoo Research PricewaterhouseCoopers Center for Advanced Research United States Department of Computer science School of Engineering and Advanced Technology Massey University New Zealand Departent of Computer science Rutgers University United States Department of Computer Science Dortmund University of Technology. Germany ILOG Rotterdam School of Management Erasmus University Netherlands Donald Bren School of Information and Computer Sciences University of California Irvine United States Depatrment of Computer science University of Southern California United States IBM Almaden Research Center United States Department of Computer Information Technology Southern New Hampshire University United States Computer and Information Technology Program College of Technology Purdue University United States Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computer Science and Engineering University of North Texas United States School of Computing DePaul University United States Department of Computer Science McGill University Canada School of Computer Science University of Waterloo Canada Department of Software and Information Systems University of North Carolina Charlotte United States University of New Hampshire United States Microsoft Research Institute for the Study of Learning and Expertise Applied Reactivity Inc. United States NASA Ames Research Center Carnegie Mellon University West

AAAI was pleased to present the AAAI-08 Workshop program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers;AI Education Workshop Colloquium;Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging;Human Implications of Human-Robot Interaction;Intelligent Techniques for Web Personalization and Recommender Systems;Metareasoning: Thinking about Thinking;Multidisciplinary Workshop on Advances in Preference Handling;Search in Artificial Intelligence and Robotics;Spatial and Temporal Reasoning;Trading Agent Design and Analysis;Transfer learning for Complex Tasks;What Went Wrong and Why: Lessons from AI Research and Applications;and Wikipedia and Artificial Intelligence: An Evolving Synergy. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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An evaluation of adaptive filtering in the context of realistic task-based information exploration

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INFORMATION PROCESSING & MANAGEMENT 2008年第2期44卷 511-533页

作者： He, Daqing Brusilovsky, Peter Ahn, Jaewook Grady, Jonathan Farzan, Rosta Peng, Yefei Yang, Yiming Rogati, Monica Univ Pittsburgh Sch Informat Sci Pittsburgh PA 15256 USA Univ Pittsburgh Intelligence Syst Program Pittsburgh PA 15260 USA Carnegie Mellon Univ Sch Comp Sci Machine Learning Dept Language Technol Inst Pittsburgh PA 15213 USA Carnegie Mellon Univ Sch Comp Sci Dept Comp Sci Pittsburgh PA 15213 USA

Exploratory search increasingly becomes an important research topic. Our interests focus on task-based information exploration, a specific type of exploratory search performed by a range of professional users, such as intelligence analysts. In this paper, we present an evaluation framework designed specifically for assessing and comparing performance of innovative information access tools created to support the work of intelligence analysts in the context of task-based information exploration. The motivation for the development of this framework came from our needs for testing systems in taskbased information exploration, which cannot be satisfied by existing frameworks. The new framework is closely tied with the kind of tasks that intelligence analysts perform: complex, dynamic, and multiple facets and multiple stages. It views the user rather than the information system as the center of the evaluation, and examines how well users are served by the systems in their tasks. The evaluation framework examines the support of the systems at users' major information access stages, such as information foraging and sense-making. The framework is accompanied by a reference test collection that has 18 tasks scenarios and corresponding passage-level ground truth annotations. To demonstrate the usage of the framework and the reference test collection, we present a specific evaluation study on CAF, an adaptive filtering engine designed for supporting task-based information exploration. This study is a successful use case of the framework, and the study indeed revealed various aspects of the information systems and their roles in supporting task-based information exploration. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： task-based information exploration exploratory search evaluation framework adaptive filtering CAFE user study intelligence analysts

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learning graph matching

Learning graph matching

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11th IEEE International Conference on Computer Vision

作者： Caetano, Tiberio S. Cheng, Li Le, Quoc V. Smola, Alex J. NICTA Stat Machine Learning Program Canberra ACT 0200 Australia

ISBN: (纸本)9781424416301

As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. There are many ways in which the problem has been formulated, but most can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility functions and a quadratic term encodes edge compatibility functions. The main research focus in this theme is about designing efficient algorithms for solving approximately the quadratic assignment problem, since it is NP-hard. In this paper, we turn our attention to the complementary problem: how to estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We present a method for learning graph matching: the training examples are pairs of graphs and the "labels" are matchings between pairs of graphs. We present experimental results with real image data which give evidence that learning can improve the performance of standard graph matching algorithms. In particular, it turns out that linear assignment with such a learning scheme may improve over state-of-the-art quadratic assignment relaxations. This finding suggests that for a range of problems where quadratic assignment was thought to be essential for securing good results, linear assignment, which is far more efficient, could be just sufficient if learning is performed. This enables speed-ups of graph matching by up to 4 orders of magnitude while retaining state-of-the-art accuracy.

关键词： Computer vision

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Reachability analysis for uncertain SSPs

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2007年第4期16卷 725-749页

作者： Buffet, Olivier Australian Natl Univ Natl ICT Australia RSISE Stat Machine Learning Program Canberra ACT Australia

Stochastic Shortest Path problems (SSPs) can be efficiently dealt with by the Real-Time Dynamic programming algorithm (RTDP). Yet, RTDP requires that a goal state is always reachable. This article presents an algorithm checking for goal reachability, especially in the complex case of an uncertain SSP where only a possible interval is known for each transition probability. This gives an analysis method for determining if SSP algorithms such as RTDP are applicable, even if the exact model is not known. As this is a time-consuming algorithm, we also present a simple process that often speeds it up dramatically. Yet, the main improvement still needed is to turn to a symbolic analysis in order to avoid a complete state-space enumeration.

关键词： stochastic shortest-path problems uncertain model reachability analysis

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Fast iterative kernel principal component analysis

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JOURNAL OF machine learning RESEARCH 2007年第8期8卷 1893-1918页

作者： Guenter, Simon Schraudolph, Nicol N. Vishwanathan, S. V. N. Australian Natl Univ Res Sch Informat Sci & Engn Canberra ACT 2601 Australia Natl ICT Australia Stat Machine Learning Program Canberra ACT Australia

We develop gain adaptation methods that improve convergence of the kernel Hebbian algorithm (KHA) for iterative kernel PCA (Kim et al., 2005). KHA has a scalar gain parameter which is either held constant or decreased according to a predetermined annealing schedule, leading to slow convergence. We accelerate it by incorporating the reciprocal of the current estimated eigenvalues as part of a gain vector. An additional normalization term then allows us to eliminate a tuning parameter in the annealing schedule. Finally we derive and apply stochastic meta-descent (SMD) gain vector adaptation (Schraudolph, 1999, 2002) in reproducing kernel Hilbert space to further speed up convergence. Experimental results on kernel PCA and spectral clustering of USPS digits, motion capture and image denoising, and image super-resolution tasks confirm that our methods converge substantially faster than conventional KHA. To demonstrate scalability, we perform kernel PCA on the entire MNIST data set.

关键词： step size adaptation gain vector adaptation stochastic meta-descent kernel Hebbian algorithm online learning

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Reachability analysis for uncertain SSPs

Reachability analysis for uncertain SSPs

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17th International Conference on Tools with Artificial Intelligence

作者： Buffet, Olivier Australian Natl Univ Natl ICT Australia RSISE Stat Machine Learning Program Canberra ACT Australia

ISBN: (纸本)0769524885

关键词： stochastic shortest-path problems uncertain model reachability analysis

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Binet-cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2007年第1期73卷 95-119页

作者： Vishwanathan, S. V. N. Smola, Alexander J. Vidal, Rene Natl ICT Australia Stat Machine Learning Program Canberra ACT 0200 Australia Johns Hopkins Univ Dept Biomed Engn Ctr Imaging Sci Baltimore MD 21218 USA

We propose a family of kernels based on the Binet-Cauchy theorem, and its extension to Fredholm operators. Our derivation provides a unifying framework for all kernels on dynamical systems currently used in machine learning, including kernels derived from the behavioral framework, diffusion processes, marginalized kernels, kernels on graphs, and the kernels on sets arising from the subspace angle approach. In the case of linear time-invariant systems, we derive explicit formulae for Computing the proposed Binet-Cauchy kernels by solving Sylvester equations, and relate the proposed kernels to existing kernels based on cepstrum coefficients and subspace angles. We show efficient methods for computing Our kernels which make them viable for the practitioner. Besides their theoretical appeal, these kernels can be used efficiently in the comparison of video sequences of dynamic scenes that can be modeled as the output of a linear time-invariant dynamical system. One advantage of our kernels is that they take the initial conditions of the dynamical systems into account. As a first example, we use our kernels to compare video sequences of dynamic textures. As a second example, we apply our kernels to the problem of clustering short clips of a movie. Experimental evidence shows superior performance of our kernels.

关键词： Binet-Cauchy theorem ARMA models and dynamical systems Sylvester equation kernel methods reproducing kernel Hilbert spaces dynamic scenes dynamic textures

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Using two-stage conditional word frequency models to model word burstiness and motivating TF-IDF

Using two-stage conditional word frequency models to model w...

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11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007

作者： Sunehag, Peter Statistical Machine Learning Program National ICT Australia Locked bag 8001 ACT 2601 Australia

Several authors have recently studied the problem of creating exchangeable models for natural languages that exhibit word burstiness. Word burstiness means that a word that has appeared once in a text should be more likely to appear again than it was to appear in the first place. In this article the different existing methods are compared theoretically through a unifying framework. New models that do not satisfy the exchangeability assumption but whose probability revisions only depend on the word counts of what has previously appeared, are introduced within this framework. We will refer to these models as two-stage conditional presence/ abundance models since they, just like some recently introduced models for the abundance of rare species in ecology, seperate the issue of presence from the issue of abundance when present. We will see that the widely used TF-IDF heuristic for information retrieval follows naturally from these models by calculating a cross-entropy. We will also discuss a connection between TF-IDF and file formats that seperate presence from abundance given presence.

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learning to rank with nonsmooth cost functions

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20th Annual Conference on Neural Information Processing Systems, NIPS 2006

作者： Burges, Christopher J.C. Ragno, Robert Viet Le, Quoc Microsoft Research One Microsoft Way Redmond WA 98052 United States Statistical Machine Learning Program NICTA ACT 2601 Australia

ISBN: (纸本)9780262195683

The quality measures used in information retrieval are particularly difficult to optimize directly, since they depend on the model scores only through the sorted order of the documents returned for a given query. Thus, the derivatives of the cost with respect to the model parameters are either zero, or are undefined. In this paper, we propose a class of simple, flexible algorithms, called LambdaRank, which avoids these difficulties by working with implicit cost functions. We describe LambdaRank using neural network models, although the idea applies to any differentiable function class. We give necessary and sufficient conditions for the resulting implicit cost function to be convex, and we show that the general method has a simple mechanical interpretation. We demonstrate significantly improved accuracy, over a state-of-the-art ranking algorithm, on several datasets. We also show that LambdaRank provides a method for significantly speeding up the training phase of that ranking algorithm. Although this paper is directed towards ranking, the proposed method can be extended to any non-smooth and multivariate cost functions.

关键词： Cost functions

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Graph kernels for disease outcome prediction from protein-protein interaction networks

Graph kernels for disease outcome prediction from protein-pr...

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13th Pacific Symposium on Biocomputing (PSB)

作者： Borgwardt, Karsten M. Kriegel, Hans-Peter Vishwanathan, S. V. N. Schraudolph, Nicol N. Ludwig Maximilians Univ Munchen Inst Comp Sci Oettingenstr 67 D-80538 Munich Germany Natl ICT Australia Stat Machine Learning Program Canberra ACT 0200 Australia

ISBN: (纸本)9812704175

It is widely believed that comparing discrepancies in the protein-protein interaction (PPI) networks of individuals will become an important tool in understanding and preventing diseases. Currently PPI networks for individuals are not available, but gene expression data is becoming easier to obtain and allows us to represent individuals by a co-integrated gene expression/protein interaction network. Two major problems hamper the application of graph kernels - state-of-the-art methods for whole-graph comparison - to compare PPI networks. First, these methods do not scale to graphs of the size of a PPI network. Second, missing edges in these interaction networks are biologically relevant for detecting discrepancies, yet, these methods do not take this into account. In this article we present graph kernels for biological network comparison that are fast to compute and take into account missing interactions. We evaluate their practical performance on two datasets of co-integrated gene expression/PPI networks.

关键词： Gene expression

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