POSTER SESSION A: Tuesday March 20, 19:30
| 121 | Tülay Adali and Hualiang Li A General Framework for Learning in the Complex Domain and its Applications |
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| 142 | Zhengdong Lu and Miguel A. Carreira-Perpinan and Cristian Sminchisescu People Tracking with the Laplacian Eigenmaps Latent Variable Model |
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| 156 | Nicolas Le Roux and Yoshua Bengio Representational Power of Restricted Boltzmann Machines and Deep Belief Networks |
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| 154 | Ronald Parr and Christopher Painter-Wakefield and Lihong Li and Michael Littman Analyzing Feature Generation for Value-Function Approximation |
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| 155 | Xuejun Liao and Hui Li and Ronald Parr and Larry Carin Regionalized Policy Representation for Reinforcement Learning in POMDPs |
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| 162 | Michael P. Holmes and Alexander G. Gray and Charles Lee Isbell, Jr. Fast nonparametric conditional density estimation |
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| 152 | O. Chapelle On Multiple Kernel Learning |
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| 148 | Christian Plagemann and Kristian Kersting and Patrick Pfaff and Wolfram Burgard Heteroscedastic Gaussian Process Regression for Modeling Range Sensors in Mobile Robotics |
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| 170 | Rich Caruana, Mohamed Elhawar, Nam Nguyen, and Casey Smith A Variations Tool for Clustering |
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| 144 | Zhengdong Lu The Laplace Approximation of Gaussian Process Mixture |
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| 151 | Narayanan Ramanathan and Rama Chellappa Aging Faces - Learning facial growth models |
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| 166 | Gang Wang and Dit-Yan Yeung and Frederick H. Lochovsky The Regularization Path for Nonlinear LASSO Regression |
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| 131 | Aurélie GOULON and Arthur DUPRAT and Gerard DREYFUS VIRTUAL LEAVE-ONE-OUT ESTIMATION OF GENERALIZATION ERROR |
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| 129 | Gaurav Aggarwal and Rama Chellappa Learning Symmetry: A Shape from Shading Approach |
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| 132 | Ricardo Silva and Edoardo Airoldi and Katherine A. Heller The role of analogies in biological data: a study in the exploratory analysis of protein-protein interactions |
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| 161 | Dragos Margineantu and Roman Fresnedo Bayesian Methods for the Evaluation of Classifiers |
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| 123 | Brijnesh J. Jain and Klaus Obermayer Learning in Structured Input and Output Spaces |
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| 167 | Yasunori Nishimori and Shotaro Akaho and Samer Abdallah and Mark D. Plumbley Geodesic Learning Algorithms Over Flag Manifolds |
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POSTER SESSION B: Wednesday March 21, 19:30
| 175 | Yasemin Altun Exploring Regularization in Learning to Predict Structured Objects |
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| 172 | PE. Barbano and M.Scoffier and D. Healy and Y. LeCun A Theoretical Framework for Measuring the Performance of Deep Belief Networks |
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| 176 | Maya R. Gupta and Eric Garcia and Erika Chin Adaptive Neighborhood Definitions for Local Learning |
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| 160 | David Minnen and Thad Starner and Irfan Essa and Charles Isbell Pattern Discovery for Locating Motifs in Multivariate, Real-valued Time-series Data |
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| 168 | Thomas M. Breuel Testing and Benchmarking Large Machine Learning Systems |
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| 134 | Pavel P. Kuksa and Vladimir Pavlovic Kernel methods for DNA barcoding |
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| 120 | Cesare Alippi and Manuel Roveri JUST-IN-TIME ADAPTIVE CLASSIFIERS |
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| 158 | Pei Yin and Irfan Essa and Jame M. Rehg The Segmental Boosting Algorithm for Time-series Feature Selection |
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| 146 | Peter Sunehag, Jochen Trumpf and Nicol Schraudolph Stochastic approximation theory for online learning with adaptive updates |
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| 140 | Minyoung Kim and Vladimir Pavlovic Discriminative State Space Models |
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| 135 | K. Pelckmans and J.A.K. Suykens and B. De Moor Transductive Learning over Graphs: Incremental Assessment |
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| 173 | Alexandru Niculescu-Mizil and Rich Caruana Task Selection for Multi-Task Bayes Net Structure Learning |
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| 136 | Arto Klami and Samuel Kaski Decomposition into local data set-specific and shared effects by a mixture of probabilistic CCAs |
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| 165 | Omid Madani Prediction Games in Infinitely Rich Worlds |
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| 147 | Antti Honkela and Matti Tornio and Tapani Raiko and Juha Karhunen Natural Gradient for Variational Bayesian Learning |
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| 127 | Sudipto Guha and Kamesh Munagala Approximation Algorithms for Budgeted Learning Problems |
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| 122 | Brijnesh J. Jain and Klaus Obermayer T-Linear Discriminant Functions |
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| 174 | Igor Durdanovic, Eric Cosatto, Hans Peter Graf Modeling SVM Parallelization |
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| 149 | L. Xu and L. SHi Model Selection and Automatic Model Selection for Statistical Learning: |
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