Contents -- Preface -- Donors -- NIPS Foundation -- Committees -- Reviewers -- An Application of Reinforcement Learning to Aerobatic Helicopter Flight -- Tighter PAC-Bayes Bounds -- Online Classification for Complex Problems Using Simultaneous Projections -- Learning on Graph with Laplacian Regularization -- Multi-Task Feature Learning -- Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning -- Efficient Methods for Privacy Preserving Face Detection -- Active learning for misspecified generalized linear models -- Subordinate class recognition using relational object models -- Unified Inference for Variational Bayesian Linear Gaussian State-Space Models -- A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems -- Sample complexity of policy search with known dynamics -- AdaBoost is Consistent -- A selective attention multi-chip system with dynamic synapses and spiking neurons -- Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks -- Convergence of Laplacian Eigenmaps -- Analysis of Representations for Domain Adaptation -- An Approach to Bounded Rationality -- Greedy Layer-Wise Training of Deep Networks -- Dirichlet-Enhanced Spam Filtering based on Biased Samples -- Detecting Humans via Their Pose -- Similarity by Composition -- Denoising and Dimension Reduction in Feature Space -- Learning to Rank with Nonsmooth Cost Functions -- Conditional mean field -- Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation -- Branch and Bound for Semi-Supervised Support Vector Machines -- Automated Hierarchy Discovery for Planning in Partially Observable Environments -- Max-margin classification of incomplete data -- Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model -- Implicit Online Learning with Kernels.

Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons -- Bayesian Ensemble Learning -- Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions -- Map-Reduce for Machine Learning on Multicore -- Relational Learning with Gaussian Processes -- Recursive Attribute Factoring -- On Transductive Regression -- Balanced Graph Matching -- Learning from Multiple Sources -- Kernels on Structured Objects Through Nested Histograms -- Differential Entropic Clustering of Multivariate Gaussians -- Support Vector Machines on a Budget -- A Theory of Retinal Population Coding -- Learning to Traverse Image Manifolds -- Using Combinatorial Optimization within Max-Product Belief Propagation -- Optimal Single-Class Classification Strategies -- A Small World Threshold for Economic Network Formation -- PG-means: learning the number of clusters in data -- Clustering Under Prior Knowledge with Application to Image Segmentation -- Multi-dynamic Bayesian Networks -- Image Retrieval and Classification Using Local Distance Functions -- Multiple Instance Learning for Computer Aided Diagnosis -- Distributed Inference in Dynamical Systems -- iLSTD: Eligibility Traces and Convergence Analysis -- A PAC-Bayes Risk Bound for General Loss Functions -- Bayesian Policy Gradient Algorithms -- Data Integration for Classification Problems Employing Gaussian Process Priors -- Approximate inference using planar graph decomposition -- Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints -- No-regret Algorithms for Online Convex Programs -- Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis -- Approximate Correspondences in High Dimensions -- A Kernel Method for the Two-Sample-Problem -- Learning Nonparametric Models for Probabilistic Imitation.

Training Conditional Random Fields for Maximum Labelwise Accuracy -- Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces -- Graph-Based Visual Saliency -- Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds -- Manifold Denoising -- TrueSkill: A Bayesian Skill Rating System -- Prediction on a Graph with a Perceptron -- Geometric entropy minimization (GEM) for anomaly detection and localization -- Single Channel Speech Separation Using Factorial Dynamics -- Correcting Sample Selection Bias by Unlabeled Data -- Sparse Representation for Signal Classification -- In-N etwork PCA and Anomaly Detection -- Learning Time-Intensity Proxles of Human Activity using Non-Parametric Bayesian Models -- Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm -- Adaptor Grammars:A Framework for Specifying Compositional Nonparametric Bayesian Models -- A Humanlike Predictor of Facial Attractiveness -- Clustering appearance and shape by learning jigsaws -- A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems -- An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models -- Combining casual and similarity-based reasoning -- A Nonparametric Approach to Bottom-Up Visual Saliency -- Hierarchical Dirichlet Processes with Random Effects -- An Information Theoretic Framework for Eukaryotic Gradient Sensing -- Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons -- Predicting spike times from subthreshold dynamics of a neuron -- Gaussian and Wishart Hyperkernels -- Causal inference in sensorimotor integration -- Multiple timescales and uncertainty in motor adaptation -- Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach.

Accelerated Variational Dirichlet Process Mixtures -- PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier -- Inducing Metric Violations in Human Similarity Judgements -- Modelling transcriptional regulation using Gaussian processes -- Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields -- Efficient sparse coding algorithms -- A Bayesian Approach to Diffusion Models of Decision-Making and Response Time -- Efficient Structure Learning of Markov Networks using L1-Regularization -- Aggregating Classification Accuracy across Time: Application to Single Trial EEG -- Uncertainty,phase and oscillatory hippocampal recall -- Blind Motion Deblurring Using Image Statistics -- Speakers optimize information density through syntactic reduction -- Real-time adaptive information-theoretic optimization of neurophysiology experiments -- Ordinal Regression by Extended Binary Classification -- Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data -- Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space -- Learnability and the Doubling Dimension -- Emergence of conjunctive visual features by quadratic independent component analysis -- Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure -- Analysis of Contour Motions -- Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions -- Dynamic Foreground/Background Extraction from Images and Videos using Random Patches -- Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning -- Statistical Modeling of Images with Fields of Gaussian Scale Mixtures -- An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments.

Isotonic Conditional Random Fields and Local Sentiment Flow -- Part-based Probabilistic Point Matching using Equivalence Constraints -- Modeling Dyadic Data with Binary Latent Factors -- Fast Discriminative Visual Codebooks using Randomized Clustering Forests -- Context Effects in Category Learning: An Investigation of Four Probabilistic Models -- Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General-Sum Stochastic Games -- N on-rigid point set registration: Coherent Point Drift -- Fundamental Limitations of Spectral Clustering -- On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts -- A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments -- Temporal dynamics of information content carried by neurons in the primary visual cortex -- Blind source separation for over-determined delayed mixtures -- The Neurodynamics of Belief Propagation on Binary Markov Random Fields -- Handling Advertisements of Unknown Quality in Search Advertising -- Bayesian Model Scoring in Markov Random Fields -- Game theoretic algorithms for Protein-DNA binding -- Bayesian Image Super-resolution, Continued -- Parameter Expanded Variational Bayesian Methods -- Inferring Network Structure from Co-Occurrences -- Unsupervised Regression with Applications to Nonlinear System Identification -- Stability of K-Means Clustering -- Learning to parse images of articulated bodies -- Efficient Learning of Sparse Representations with an Energy-Based Model -- Learning to be Bayesian without Supervision -- Boosting Structured Prediction for Imitation Learning -- Large Scale Hidden Semi-Markov SVMs -- Natural Actor-Critic for Road Traffic Optimisation -- Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees -- Learning annotated hierarchies from relational data.

Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds.