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NIPS Online

Learning@Snowbird 2004, Program

Invited Speakers

  • Piotr Indyk, MIT, "Algorithmic Applications of Low-Distortion Embeddings".
  • Larry Jackel, DARPA, "Robot Navigation, present and future".
  • Lior Pachter, UC Berkeley, "Parametric inference for biological sequence analysis".
  • Eero Simoncelli, NYU, "Learning the Statistical Structure of the Visual World".
  • Shimon Ullman, Weizman Institute, "Visual object classification, recognition and segmentation".
  • Jean-Philippe Vert, Ecole des Mines, "Kernel methods in computational biololgy".
  • Andrew Zisserman, Oxford, "Object class recognition using trainable visual models".

Oral Presentations

005Ahmad Emami and Frederick Jelinek, CLSP, Johns Hopkins University
A Neural Syntactic Language Model
007Eric B. Baum
What is Thought?
010John Lafferty, Yan Liu, and Xiaojin Zhu (Carnegie Mellon University)
Kernel Conditional Random Fields: Representation, Clique Selection, and Semi-Supervised Learning
011Bill Triggs and Ankur Agarwal (GRAVIR-CNRS-INRIA, Grenoble, France)
Learning to Reconstruct 3D Human Pose and Motion from Silhouettes
015Y. Lin, D. D. Lee, and L. K. Saul (U. Pennsylvania)
Generative Models for Auditory Localization
021M. Paskin (U. California, Berkeley), and C. Guestrin (Intel Berkeley Research Center)
Distributed Inference in Sensor Networks
023S. Lazebnik (Beckman Institute, U. Illinois), C. Schmid (INRIA, Rhone-Alpes), and J. Ponce (Beckman Institute, U. Illinois)
Learning Local Affine-Invariant Part Representations for Object Recognition
024H.J. Kappen and J. Mooij (University of Nijmegen)
Validity estimates for belief propagation on real-world networks
025D. Heckerman, C. Meek (Microsoft), and D. Koller (Stanford)
Probabilistic Models for Relational Data
036R. Collobert (IDIAP), and S. Bengio (IDIAP)
MLP = (SVM)^2
041S. Kumar (CMU), and M. Hebert (CMU)
Multiclass Discriminative Random Fields for Object Detection
043Fu Jie Huang (Courant Institute, NYU), Yann LeCun (Courant Institute, NYU), Leon Bottou (NEC)
Learning to Recognize Object Categories with Invariance Pose, Illumination, and Clutter
047Y.W. Teh (UC Berkeley), M.I. Jordan (UC Berkeley), M.J. Beal (U Toronto), D.M. Blei (UC Berkeley)
Hierarchical Dirichlet Processes
048J. Goldstein, J.C. Platt, C.J.C. Burges (Microsoft Research)
Redundant Bit Vectors for Searching High-Dimensional Regions
051D. Anguelov (Stanford U), D. Koller (Stanford U), P. Srinivasan (Stanford U), S. Thrun (Stanford U), H. Pang (Stanford U) and J. Davis(Honda Research Institute)
The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces
054R. Tedrake (MIT), T.W. Zhang (MIT), M. Fong (MIT), and H.S. Seung (MIT & Howard Hughes)
Learning to Walk by Actuating a Passive Dynamic Walker
055Ben Taskar (Stanford), and Vassil Chatalbashev (Stanford), and Daphne Koller (Stanford)
Associative Markov Networks
062A. Cutler (Utah State U.) Leo Breiman (UC Berkeley)
Visualizing Random Forests
063Yann LeCun (Courant Institute, NYU), Eric Cosatto, Jan Ben, Urs Muller, Beat Flepp (Net-Scale Technologies)
End-to-End Learning of Vision-Based Obstacle Avoidance for Off-Road Robots
065N. Jojic, V. Jojic, D. Heckerman, C. Meek (Microsoft Research)
Graphical models for rational design of AIDS vaccine cocktails
071Trevor Hastie (Stanford U), Saharon Rosset (IBM, Yorktown) and Ji Zhu (U. Michigan)
The Entire Regularization Path for the Support Vector Machine
079Q. Morris (U. Toronto), B. Frey (U Toronto), O Shai (U Toronto)
Gene function prediction using mouse gene expression profiles
084B. J. Frey (University of Toronto) and N. Jojic (Microsoft Research)
Learning the "epitome" of an image
086Craig G. Nevill-Manning (Google) and Ian H. Witten (U Waikato, New Zealand)
Extracting Structured Data from Database-Backed Web Sites using Sequence Alignment
087Margarita Osadchy (NEC), Matthew Miller (NEC), Yann LeCun (Courant Institute, NYU)
Synergistic Face Detection and Pose Estimation


001J. Langford (TTI-Chicago)
The Method of Reduction in Machine Learning
002C.Alippi (Politecnico di Milano), and F.Scotti (University of Milan)
A Combination of Multiple Classifier Design for Low-Complex, Highly Performing and Power-Aware Classifiers
003A.Karatzoglou D. Meyer (U. Tech. Vienna) A.Zeileis K.Hornik (B. U. Vienna)
kernlab - A kernel methods package for R
004J. Kaufhold (GE Global Research Center), and A. Hoogs (GE Global Research Center)
Learning to Segment Images Using Region-Based Perceptual Edge Features
006C. Raphael
Musical Score Following with Latent Tempo Variables
008R. Kozma (U. Memphis), W.J. Freeman (UC Berkeley), P.Erdi (Kalamazoo & KFKI Hungary)
Learning in the KIV Dynamic Neural Network Model of the Cortico-Hippocampal Formation: Biological Basis and Computational Applications
009Sumit Basu (Microsoft Research), and Tanzeem Choudhury (Intel Research)
Learning Relationships from Conversational Patterns
012M. Embrechts (Rensselaer Polytechnic Institute)
Direct Kernel Methods for Molecular Design: Kernel Centering, Feature Selection and Regularization
013V. Jojic (Microsoft Research), N. Jojic (Microsoft Research), C. Meek (Microsoft Research), D. Geiger (Technion, Israel), A. Siepel (UC Santa Cruz.), D. Haussler (UC Santa Cruz.), D. Heckerman (Microsoft Research)
Efficient Approximations for Learning Phylogenetic HMM Models from Data
014Ciprian Chelba (Microsoft Research) and Alex Acero (Microsoft Research)
Conditional Maximum Likelihood Estimation using Rational Function Growth Transform
016S. Akaho (AIST)
The e-PCA and m-PCA: Dimension Reduction by Information Geometry
017P. Haffner (AT&T labs-research)
An equivalence between the thermometer representation of
026Lyle Ungar, Dean Foster and Bob Stine (U. of Pennsylvania)
Streaming Feature Selection
027V. Petrushin (Accenture Technology Labs)
Adaptive Algorithm for Pitch-synchronous Speech Signal Segmentation
029N. Intrator (Brown U) and N. Neretti (Brown U) and Leon N Cooper
Robust Statistics from multiple pings improves noise tolerance in sonar
030M. Seeger (U. California, Berkeley), and M. I. Jordan (U. California, Berkeley)
Fast Sparse Gaussian Process Classification with Multiple Classes
032S. Matwin (Univ. of Ottawa), E. Alphonse (INRA - France), N. Stroppa (ENST - France)
Relational Feature Selection
034S. Lenser (CMU) and M. Veloso (CMU)
Time Series Classification Using Non-Parametric Statistics
035B. Upcroft (U. Sydney), S. Kumar (U. Sydney), H. Durrant-Whyte (U. Sydney)
Unsupervised Data Association in Unstructured Outdoor Environments
037H. Guo, A. Rangarajan (U. Florida), S. Joshi (UNC Chapel Hill) and L. Younes (Johns Hopkins)
Diffeomorphic point matching for statistical shape analysis
038Y. Oussar (ESPCI), G. Dreyfus (ESPCI)
Generalized leverages and generalization error
039Jianxin Wu (Georgia Tech), Matthew D. Mullin (Georgia Tech), James M. Rehg (Georgia Tech)
Linear Programming Ensemble for Classifiers with Highly
040R. Kondor (Columbia), T. Jebara (Columbia), G. Csanyi (U. Cambridge), E. Ahnert (U. Cambridge)
Learning from Derivatives and other Linear functionals
042Y. Bengio (U. Montreal), O. Delalleau (U. Montreal), N. LeRoux (U. Montreal)
Non-parametric function induction in semi-supervised learning
044A. Banerjee (U. Texas at Austin), I. Dhillon (U. Texas at Austin), J. Ghosh (U. Texas at Austin), S. Merugu (U. Texas at Austin)
Rate Distortion, Bregman Divergences and Maximum Likelihood Mixture Estimation
045S. Roweis (U. Toronto)
Nonlinear Sensor Fusion Networks
046Joydeep Ghosh (U. Texas), Suju Rajan (U.Texas) and Melba Crawford (U. Texas/Center for Space Res.)
Automatic Generation of Class Hierarchies for High-Dimensional Multiclass Problems
049D. Ross (U. Toronto), J. Lim (U of Illinois), and M.-H. Yang (Honda Research Institute)
Adaptive Probablistic Visual Tracking with Incremental Subspace Upldat
052T. Jebara (Columbia University) and Y. Bengio (Universite de Montreal)
Orbit Learning using Convex Optimization
053Austin I. Eliazar and Ronald Parr
Learning Probabilistic Robot Motion Models
056R. Caruana (Cornell), and A. Niculescu-Mizil (Cornell)
An Empirical Comparison of Supervised Learning Methods Using Nine Performance Criteria
059B. Kegl, L. Wang (University of Montreal)
Adaptive regularization of base classifiers in boosting
061O. Madani and D. M. Pennock and G. W. Flake (Yahoo! Research Labs)
Co-Validation: Using Model Disagreement on Unlabeled Data for Estimating Prediction Error and Variance
064Greg Grudic (University of Colorado at Boulder)
Basis Function Regression and Classification Models with Probabilistic Predictions
068M. Reyes-Gomez (Columbia University), N. Jojic (Microsoft Research) and D.P.W. Ellis (Columbia University)
Detailed graphical models for source separataion and missing data interpolation in audio
070J. Lim (U of Illinois), J. Ho (UCSD), M-H Yang (Honda Research Institute), K-L Lee (UIUC), David Kriegman (UCSD)
Image Clustering wiht Metric, Local Linear Structure and Affine Symmetry
072S.C. Kremer (U. Guelph)
Stochastic Correlative Learning Algorithms for Recurrent Network Learning
074M. Alex O. Vasilescu and Demetri Terzopoulos, NYU, UofT
Multilinear Independent Components Analysis
075M.J. Beal (U Toronto), Y.W. Teh (UC Berkeley), and M.I. Jordan (UC Berkeley)
Infinite Hidden Markov Models via the Hierarchical Dirichlet Process
076A. Silvescu (Iowa State U.), and V. Honavar (Iowa State U.)
A Graphical Model for Shallow Parsing Sequences
078M. Oresic (VTT Biotechnology)
Characterization of physiological states using metabolic profiling approaches
080J. Moody, Y. Liu, (International Computer Science Institute), M. Saffell and K. Youn (OGI School of Science and Engineering)
Stochastic Direct Reinforcement: Representations, Recurrence and Stochastic Games
081H. Attias (HT Attias Inc) and Matthew J. Beal (U. Toronto)
Tree of Latent Mixtures for Bayesian Modelling and Classification of High Dimensional Data
082S. Nagarajan (UCSF), M. Sahani (UCSF), H. Attias (HT Attias Inc)
A Graphical Model for Electromagnetic Source Imaging
083Sara C. Madeira (UBI, Portugal) and Arlindo L. Oliveira (IST/INESC-ID, Portugal)
Biclustering Algorithms for Biological Data Analysis
089E. Glover (NEC-Labs), B. Klock (NEC-Labs), D. Berton (NEC-Labs)
Building a Personalizable Genre-Based Metasearch Engine that is Easy to Train

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