Learning Workshop, Puerto Rico, March 19-22, 2007

POSTERS

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
[DjVu|PDF]
142 Zhengdong Lu and Miguel A. Carreira-Perpinan and Cristian Sminchisescu
People Tracking with the Laplacian Eigenmaps Latent Variable Model
[DjVu|PDF]
156 Nicolas Le Roux and Yoshua Bengio
Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
[DjVu|PDF]
154 Ronald Parr and Christopher Painter-Wakefield and Lihong Li and Michael Littman
Analyzing Feature Generation for Value-Function Approximation
[DjVu|PDF]
155 Xuejun Liao and Hui Li and Ronald Parr and Larry Carin
Regionalized Policy Representation for Reinforcement Learning in POMDPs
[DjVu|PDF]
162 Michael P. Holmes and Alexander G. Gray and Charles Lee Isbell, Jr.
Fast nonparametric conditional density estimation
[DjVu|PDF]
152 O. Chapelle
On Multiple Kernel Learning
[DjVu|PDF]
148 Christian Plagemann and Kristian Kersting and Patrick Pfaff and Wolfram Burgard
Heteroscedastic Gaussian Process Regression for Modeling Range Sensors in Mobile Robotics
[DjVu|PDF]
170 Rich Caruana, Mohamed Elhawar, Nam Nguyen, and Casey Smith
A Variations Tool for Clustering
[DjVu|PDF]
144 Zhengdong Lu
The Laplace Approximation of Gaussian Process Mixture
[DjVu|PDF]
151 Narayanan Ramanathan and Rama Chellappa
Aging Faces - Learning facial growth models
[DjVu|PDF]
166 Gang Wang and Dit-Yan Yeung and Frederick H. Lochovsky
The Regularization Path for Nonlinear LASSO Regression
[DjVu|PDF]
131 Aurélie GOULON and Arthur DUPRAT and Gerard DREYFUS
VIRTUAL LEAVE-ONE-OUT ESTIMATION OF GENERALIZATION ERROR
[DjVu|PDF]
129 Gaurav Aggarwal and Rama Chellappa
Learning Symmetry: A Shape from Shading Approach
[DjVu|PDF]
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
[DjVu|PDF]
161 Dragos Margineantu and Roman Fresnedo
Bayesian Methods for the Evaluation of Classifiers
[DjVu|PDF]
123 Brijnesh J. Jain and Klaus Obermayer
Learning in Structured Input and Output Spaces
[DjVu|PDF]
167 Yasunori Nishimori and Shotaro Akaho and Samer Abdallah and Mark D. Plumbley
Geodesic Learning Algorithms Over Flag Manifolds
[DjVu|PDF]

POSTER SESSION B: Wednesday March 21, 19:30

175 Yasemin Altun
Exploring Regularization in Learning to Predict Structured Objects
[DjVu|PDF]
172 PE. Barbano and M.Scoffier and D. Healy and Y. LeCun
A Theoretical Framework for Measuring the Performance of Deep Belief Networks
[DjVu|PDF]
176 Maya R. Gupta and Eric Garcia and Erika Chin
Adaptive Neighborhood Definitions for Local Learning
[DjVu|PDF]
160 David Minnen and Thad Starner and Irfan Essa and Charles Isbell
Pattern Discovery for Locating Motifs in Multivariate, Real-valued Time-series Data
[DjVu|PDF]
168 Thomas M. Breuel
Testing and Benchmarking Large Machine Learning Systems
[DjVu|PDF]
134 Pavel P. Kuksa and Vladimir Pavlovic
Kernel methods for DNA barcoding
[DjVu|PDF]
120 Cesare Alippi and Manuel Roveri
JUST-IN-TIME ADAPTIVE CLASSIFIERS
[DjVu|PDF]
158 Pei Yin and Irfan Essa and Jame M. Rehg
The Segmental Boosting Algorithm for Time-series Feature Selection
[DjVu|PDF]
146 Peter Sunehag, Jochen Trumpf and Nicol Schraudolph
Stochastic approximation theory for online learning with adaptive updates
[DjVu|PDF]
140 Minyoung Kim and Vladimir Pavlovic
Discriminative State Space Models
[DjVu|PDF]
135 K. Pelckmans and J.A.K. Suykens and B. De Moor
Transductive Learning over Graphs: Incremental Assessment
[DjVu|PDF]
173 Alexandru Niculescu-Mizil and Rich Caruana
Task Selection for Multi-Task Bayes Net Structure Learning
[DjVu|PDF]
136 Arto Klami and Samuel Kaski
Decomposition into local data set-specific and shared effects by a mixture of probabilistic CCAs
[DjVu|PDF]
165 Omid Madani
Prediction Games in Infinitely Rich Worlds
[DjVu|PDF]
147 Antti Honkela and Matti Tornio and Tapani Raiko and Juha Karhunen
Natural Gradient for Variational Bayesian Learning
[DjVu|PDF]
127 Sudipto Guha and Kamesh Munagala
Approximation Algorithms for Budgeted Learning Problems
[DjVu|PDF]
122 Brijnesh J. Jain and Klaus Obermayer
T-Linear Discriminant Functions
[DjVu|PDF]
174 Igor Durdanovic, Eric Cosatto, Hans Peter Graf
Modeling SVM Parallelization
[DjVu|PDF]
149 L. Xu and L. SHi
Model Selection and Automatic Model Selection for Statistical Learning:
[DjVu|PDF]