Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference

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Bernhard Schölkopf, John Platt, Thomas Hofmann
MIT Press, 2007 - Computers - 1643 pages
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The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. It draws a diverse group of attendees -- physicists, neuroscientists, mathematicians, statisticians, and computer scientists -- interested in theoretical and applied aspects of modeling, simulating, and building neural-like or intelligent systems. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December 2006 meeting, held in Vancouver.

 

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Contents

An Application of Reinforcement Learning to Aerobatic Helicopter Flight
1
Tighter PACBayes Bounds
9
Online Classification for Complex Problems Using Simultaneous Projections
17
Learning on Graph with Laplacian Regularization
25
MultiTask Feature Learning
41
Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning
49
Efficient Methods for Privacy Preserving Face Detection
57
Active learning for misspecified generalized linear models
65
Uncertaintyphase and oscillatory hippocampal recall
833
Blind Motion Deblurring Using Image Statistics
841
Speakers optimize information density through syntactic reduction
849
Realtime adaptive informationtheoretic optimization of neurophysiology experiments
857
Ordinal Regression by Extended Binary Classification
865
A Sketchbased Sampling Technique for Sparse Data
873
Generalized Regularized LeastSquares Learning with Predefined Features in a Hilbert Space
881
Learnability and the Doubling Dimension
889

Subordinate class recognition using relational object models
73
Unified Inference for Variational Bayesian Linear Gaussian StateSpace Models
81
A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems
89
Sample complexity of policy search with known dynamics
97
AdaBoost is Consistent
105
A selective attention multichip system with dynamic synapses and spiking neurons
113
Temporal and CrossSubject Probabilistic Models for fMRI Prediction Tasks
121
Convergence of Laplacian Eigenmaps
129
Analysis of Representations for Domain Adaptation
137
An Approach to Bounded Rationality
145
Greedy LayerWise Training of Deep Networks
153
DirichletEnhanced Spam Filtering based on Biased Samples
161
Detecting Humans via Their Pose
169
Similarity by Composition
177
Denoising and Dimension Reduction in Feature Space
185
Learning to Rank with Nonsmooth Cost Functions
193
Conditional mean field
201
Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation
209
Branch and Bound for SemiSupervised Support Vector Machines
217
Automated Hierarchy Discovery for Planning in Partially Observable Environments
225
Maxmargin classification of incomplete data
233
Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model
241
Implicit Online Learning with Kernels
249
Context dependent amplification of both rate and eventcorrelation in a VLSI network of spiking neurons
257
Bayesian Ensemble Learning
265
Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions
273
MapReduce for Machine Learning on Multicore
281
Relational Learning with Gaussian Processes
289
Recursive Attribute Factoring
297
On Transductive Regression
305
Balanced Graph Matching
313
Learning from Multiple Sources
321
Kernels on Structured Objects Through Nested Histograms
329
Differential Entropic Clustering of Multivariate Gaussians
337
Support Vector Machines on a Budget
345
A Theory of Retinal Population Coding
353
Learning to Traverse Image Manifolds
361
Using Combinatorial Optimization within MaxProduct Belief Propagation
369
Optimal SingleClass Classification Strategies
377
A Small World Threshold for Economic Network Formation
385
learning the number of clusters in data
393
Clustering Under Prior Knowledge with Application to Image Segmentation
401
Multidynamic Bayesian Networks
409
Image Retrieval and Classification Using Local Distance Functions
417
Multiple Instance Learning for Computer Aided Diagnosis
425
Distributed Inference in Dynamical Systems
433
Eligibility Traces and Convergence Analysis
441
A PACBayes Risk Bound for General Loss Functions
449
Bayesian Policy Gradient Algorithms
457
Data Integration for Classification Problems Employing Gaussian Process Priors
465
Approximate inference using planar graph decomposition
473
NearUniform Sampling of Combinatorial Spaces Using XOR Constraints
481
Noregret Algorithms for Online Convex Programs
489
Large Margin Multichannel AnalogtoDigital Conversion with Applications to Neural Prosthesis
497
Approximate Correspondences in High Dimensions
505
A Kernel Method for the TwoSampleProblem
513
Learning Nonparametric Models for Probabilistic Imitation
521
Training Conditional Random Fields for Maximum Labelwise Accuracy
529
Adaptive Spatial Filters with predefined Region of Interest for EEG based BrainComputerInterfaces
537
GraphBased Visual Saliency
545
Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds
553
Manifold Denoising
561
A Bayesian Skill Rating System
569
Prediction on a Graph with a Perceptron
577
Geometric entropy minimization GEM for anomaly detection and localization
585
Single Channel Speech Separation Using Factorial Dynamics
593
Correcting Sample Selection Bias by Unlabeled Data
601
Sparse Representation for Signal Classification
609
InN etwork PCA and Anomaly Detection
617
Learning TimeIntensity Proxles of Human Activity using NonParametric Bayesian Models
625
Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm
633
A Framework for Specifying Compositional Nonparametric Bayesian Models
641
A Humanlike Predictor of Facial Attractiveness
649
Clustering appearance and shape by learning jigsaws
657
A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems
665
An Efficient Method for GradientBased Adaptation of Hyperparameters in SVM Models
673
Combining casual and similaritybased reasoning
681
A Nonparametric Approach to BottomUp Visual Saliency
689
Hierarchical Dirichlet Processes with Random Effects
697
An Information Theoretic Framework for Eukaryotic Gradient Sensing
705
Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons
713
Predicting spike times from subthreshold dynamics of a neuron
721
Gaussian and Wishart Hyperkernels
729
Causal inference in sensorimotor integration
737
Multiple timescales and uncertainty in motor adaptation
745
A Clustering Approach
753
Accelerated Variational Dirichlet Process Mixtures
761
PACBayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier
769
Inducing Metric Violations in Human Similarity Judgements
777
Modelling transcriptional regulation using Gaussian processes
785
SemiSupervised Discriminative Random Fields
793
Efficient sparse coding algorithms
801
A Bayesian Approach to Diffusion Models of DecisionMaking and Response Time
809
Efficient Structure Learning of Markov Networks using L1Regularization
817
Application to Single Trial EEG
825
Emergence of conjunctive visual features by quadratic independent component analysis
897
Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure
905
Analysis of Contour Motions
913
Attributeefficient learning of decision lists and linear threshold functions under unconcentrated distributions
921
Dynamic ForegroundBackground Extraction from Images and Videos using Random Patches
929
Effects of Stress and Genotype on Metaparameter Dynamics in Reinforcement Learning
937
Statistical Modeling of Images with Fields of Gaussian Scale Mixtures
945
An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments
953
Isotonic Conditional Random Fields and Local Sentiment Flow
961
Partbased Probabilistic Point Matching using Equivalence Constraints
969
Modeling Dyadic Data with Binary Latent Factors
977
Fast Discriminative Visual Codebooks using Randomized Clustering Forests
985
An Investigation of Four Probabilistic Models
993
Approximating the Set of Subgame Perfect Equilibria in GeneralSum Stochastic Games
1001
Coherent Point Drift
1009
Fundamental Limitations of Spectral Clustering
1017
On the Relation Between Low Density Separation Spectral Clustering and Graph Cuts
1025
A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments
1033
Temporal dynamics of information content carried by neurons in the primary visual cortex
1041
Blind source separation for overdetermined delayed mixtures
1049
The Neurodynamics of Belief Propagation on Binary Markov Random Fields
1057
Handling Advertisements of Unknown Quality in Search Advertising
1065
Bayesian Model Scoring in Markov Random Fields
1073
Game theoretic algorithms for ProteinDNA binding
1081
Bayesian Image Superresolution Continued
1089
Parameter Expanded Variational Bayesian Methods
1097
Inferring Network Structure from CoOccurrences
1105
Unsupervised Regression with Applications to Nonlinear System Identification
1113
Stability of KMeans Clustering
1121
Learning to parse images of articulated bodies
1129
Efficient Learning of Sparse Representations with an EnergyBased Model
1137
Learning to be Bayesian without Supervision
1145
Boosting Structured Prediction for Imitation Learning
1153
Large Scale Hidden SemiMarkov SVMs
1161
Natural ActorCritic for Road Traffic Optimisation
1169
Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees
1177
Learning annotated hierarchies from relational data
1185
Shifting OneInclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds
1193
Neurophysiological Evidence of Cooperative Mechanisms for Stereo Computation
1201
Robotic Grasping of Novel Objects
1209
Theory and Dynamics of Perceptual Bistability
1217
Fast Iterative Kernel PCA
1225
CrossValidation Optimization for Large Scale Hierarchical Classification Kernel Methods
1233
Information Bottleneck for Non CoOccurrence Data
1241
Large Margin Hidden Markov Models for Automatic Speech Recognition
1249
Nonlinear physicallybased models for decoding motorcortical population activity
1257
Convex Repeated Games and Fenchel Duality
1265
Recursive ICA
1273
Chained Boosting
1281
A recipe for optimizing a timehistogram Hideaki Shimazaki
1289
Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype
1297
Modeling Genetic Recombination in Open Ancestral Space
1305
Learning Dense 3D Correspondence
1313
An Oracle Inequality for Clipped Regularized Risk Minimizers
1321
Learning Structural Equation Models for fMRI
1329
Mixture Regression for Covariate Shift
1337
Modeling Human Motion Using Binary Latent Variables
1345
A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation
1353
Towards a general independent subspace analysis
1361
Linearlysolvable Markov decision problems
1369
Logistic Regression for Single Trial EEG Classification
1377
Large Margin Component Analysis
1385
Learning Motion Style Synthesis from Perceptual Observations
1393
LargeScale Sparsified Manifold Regularization
1401
Scalable Discriminative Learning for Natural Language Parsing and Translation
1409
Generalized Maximum Margin Clustering and Unsupervised Kernel Learning
1417
A ComplexityDistortion Approach to Joint Pattern Alignment
1425
Online Clustering of Moving Hyperplanes
1433
Comparative Gene Prediction using Conditional Random Fields
1441
Fast Computation of Graph Kernels
1449
Temporal Coding using the Response Properties of Spiking Neurons
1457
HighDimensional Graphical Model Selection Using l1 Regularized Logistic Regression
1465
Attentional Processing on a SpikeBased VLSI Neural Network
1473
Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension
1481
Graph Laplacian Regularization for LargeScale Semidefinite Programming
1489
A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo
1497
Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization
1505
Particle Filtering for Nonparametric Bayesian Matrix Factorization
1513
A Scalable Machine Learning Approach to Go
1521
A Local Learning Approach for Clustering
1529
The RobustnessPerformance Tradeoff in Markov Decision Processes
1537
Optimal ChangeDetection and Spiking Neurons
1545
Stochastic Relational Models for Discriminative Link Prediction
1553
Nonnegative Sparse PCA
1561
Doubly Stochastic Normalization for Spectral Clustering
1569
Simplifying Mixture Models through Function Approximation
1577
Hyperparameter Learning for Graph Based Semisupervised Learning Algorithms
1585
Modified Locally Linear Embedding Using Multiple Weights
1593
Clustering Classification and Embedding
1601
MultiInstance MultiLabel Learning with Application to Scene Classification
1609
Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing
1617
A Probabilistic Algorithm Integrating Source Localization and Noise Suppression of MEG and EEG Data
1625
Subject Index
1633
Author Index
1639
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About the author (2007)

Bernhard Sch lkopf is Managing Director of the Max Planck Institute for Biological Cybernetics in T bingen, Germany, Professor for Machine Learning at the Technical University of Berlin, and General Chair of the 2006 NIPS conference.

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