NIPS 2008 Papers

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Supervised Bipartite Graph Inference

Automatic online tuning for fast Gaussian summation

The Recurrent Temporal Restricted Boltzmann Machine

Using Bayesian Dynamical Systems for Motion Template Libraries

Cyclizing Clusters via Zeta Function of a Graph

Hierarchical Conditional Random Fields for Recursive Sequential Data

Theory of matching pursuit

Estimating vector fields using sparse basis field expansions

Implicit Mixtures of Restricted Boltzmann Machines

Bayesian Experimental Design of Magnetic Resonance Imaging Sequences

Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes

Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks

Fitted Q-iteration by Advantage Weighted Regression

Unifying the Sensory and Motor Components of Sensorimotor Adaptation

Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing

QUIC-SVD: Fast SVD Using Cosine Trees

Nonparametric Bayesian Sparse Hierarchical Factor Modeling and Regression

Skill Characterization Based on Betweenness

Resolution Limits of Sparse Coding in High Dimensions

Estimating Robust Query Models with Convex Optimization

Learning Taxonomies by Dependence Maximization

On-Line Prediction on Large Diameter Graphs

Transfer Learning by Distribution Matching for Targeted Advertising

Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \ell_1-regularizedMLE

Regularized Policy Iteration

An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering

Suppport Vector Machines with a Reject Option

On Bootstrapping the ROC Curve

DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification

Privacy-preserving logistic regression

Bio-inspired Real Time Sensory Map Realignment in a Robotic Barn Owl

Rademacher Complexity Bounds for Non-I.I.D. Processes

Risk Bounds for Randomized Sample Compressed Classifiers

Structure Learning in Human Sequential Decision-Making

Counting Solution Clusters Using Belief Propagation

SDL: Supervised Dictionary Learning

Supervised Exponential Family Principal Component Analysis via Convex Optimizatio

Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text

(Not) Sparse Coding

Relative Margin Machines

Asynchronous Distributed Learning of Topic Models

Regularized Learning with Networks of Features

Probabilistic detection of short events, with application to critical care monitoring

Depression: an RL formulation and a behavioural test

Unsupervised Bayesian Parameter Estimation for Probabilistic Grammars

Spectral Hashing

Human Active Learning

Grouping Contours Via a Related Image

A Convex Upper Bound on the Log-Partition Function

Online Models for Content Optimization

Regularized Co-Clustering with Dual Supervision

Clustering via LP-based Stabilities

Dependent Dirichlet Process Spike Sorting

Designing neurophysiology experiments to optimally constrain receptive field models along parametric

Competing RBM density models for classification of fMRI images

Bounding Performance Loss in Approximate MDP Homomorphisms

Fast Rates for Regularized Objectives

Estimation of Information Theoretic Measures for Continuous Random Variables

A Scalable Hierarchical Distributed Language Model

Tighter Bounds for Structured Estimation

Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform

Comparing model predictions of response bias and variance in cue combination

Non-parametric Regression Between Manifolds

Kernel Change-point Analysis

Extended Grassmann Kernels for Subspace-Based Learning

Overlaying classifiers: a practical approach for optimal ranking

Modeling human function learning with Gaussian processes

Particle Filter-based Policy Gradient in POMDPs

Empirical performance maximization for linear rank statistics

Adaptive Template Matching with Shift-Invariant Semi-NMF

Learning Hybrid Models for Image Annotation with Partially Labeled Data

Predictive Indexing for Fast Search

Biasing Approximate Dynamic Programming with a Lower Discount Factor

A spatially varying two-sample recombinant coalescent, with applications to HIV escape response

Diffeomorphic Dimensionality Reduction

MCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features

Generative and Discriminative Learning with Unknown Labeling Bias

Mortal Multi-Armed Bandits

A mixture model for the evolution of gene expression in non-homogeneous datasets

Online Optimization in X-Armed Bandits

Playing Pinball with non-invasive BCI

PSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning

An empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis

Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets

Multi-stage Convex Relaxation for Learning with Sparse Regularization

On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization

Convergence and Rate of Convergence of A Manifold-Based Dimension Reduction

A Bayesian Approach for Extracting State Transition Dynamics from Multiple Spike Trains

Non-stationary dynamic Bayesian networks

Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation

A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approxi

LOOPS: Localizing Object Outlines using Probabilistic Shape

Cell Assemblies in Large Sparse Inhibitory Networks of Biologically Realistic Spiking Neurons

MDPs with Non-Deterministic Policies

Efficient Exact Inference in Planar Ising Models

On the equivalence between TD learning and differential Hebbian learning using a local third factor

A computational model of hippocampal function in trace conditioning

Short-Term Depression in VLSI Stochastic Synapse

Hierarchical Fisher Kernels for Longitudinal Data

Multiscale Random Fields with Application to Contour Grouping

Reconciling Real Scores with Binary Comparisons: A Unified Logistic Model for Ranking

Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method

Structured ranking learning using cumulative distribution networks

Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity

Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning

Correlated Bigram LSA for Unsupervised Language Model Adaptation

Integrating Locally Learned Causal Structures with Overlapping Variables

Sparsity of SVMs that use the epsilon-insensitive loss

Temporal Dynamics of Cognitive Control

Natural Image Denoising with Convolutional Networks

Local Gaussian Process Regression for Real Time Online Model Learning

An Homotopy Algorithm for the Lasso with Online Observations

An Extended Level Method for Efficient Multiple Kernel Learning

An ideal observer model of infant object perception

Variational Mixture of Gaussian Process Experts

Self-organization using dynamical synapses

Bayesian Kernel Shaping for Learning Control

Kernel-ARMA for Hand Tracking and Brain-Machine interfacing During 3D Motor Control

Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection

Model selection and velocity estimation using novel priors for motion patterns

Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes

Linear Classification and Selective Sampling Under Low Noise Conditions

Partially Observed Maximum Entropy Discrimination Markov Networks

Robust Near-Isometric Matching via Structured Learning of Graphical Models

Bayesian Synchronous Grammar Induction

Sparse Convolved Gaussian Processes for Multi-ouptut Regression

Bayesian Model of Behaviour in Economic Games

Tracking Changing Stimuli in Continuous Attractor Neural Networks

Dependence of Orientation Tuning on Recurrent Excitation and Inhibition in a Network Model of V1

Hebbian Learning of Bayes Optimal Decisions

Adapting to a Market Shock: Optimal Sequential Market-Making

From Online to Batch Learning with Cutoff-Averaging

Continuously-adaptive discretization for message-passing algorithms

Dimensionality Reduction for Data in Multiple Feature Representations

Goal-directed decision making in prefrontal cortex: a computational framework

Unlabeled data: Now it helps, now it doesn't

Online Metric Learning and Fast Similarity Search

Fast Prediction on a Tree

MAS: a multiplicative approximation scheme for probabilistic inference

Simple Local Models for Complex Dynamical Systems

The Mondrian Process

Inferring rankings under constrained sensing

Reducing statistical dependencies in natural signals using radial Gaussianization

Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex

Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models

Influence of graph construction on graph-based clustering measures

Nonrigid Structure from Motion in Trajectory Space

Multi-Level Active Prediction of Useful Image Annotations for Recognition

Global Ranking Using Continuous Conditional Random Fields

Measures of Clustering Quality: A Working Set of Axioms for Clustering

Stress, noradrenaline, and realistic prediction of mouse behaviour using reinforcement learning

Characteristic Kernels on Groups and Semigroups

Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree

On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing

Modeling the effects of memory on human online sentence processing with particle filters

Learning to Use Working Memory in Partially Observable Environments through Dopaminergic Reinforcement

An interior-point stochastic approximation method and an L1-regularized delta rule

Policy Search for Motor Primitives in Robotics

Load and Attentional Bayes

Cascaded Classification Models: Combining Models for Holistic Scene Understanding

Sparse Signal Recovery Using Markov Random Fields

Improving on Expectation Propagation

Clusters and Coarse Partitions in LP Relaxations

One sketch for all: Theory and Application of Conditional Random Sampling

Sparse probabilistic projections

An improved estimator of Variance Explained in the presence of noise

Fast Computation of Posterior Mode in Multi-Level Hierarchical Models

Bounds on marginal probability distributions

Phase transitions for high-dimensional joint support recovery

Semi-supervised Learning with Weakly-Related Unlabeled Data : Towards Better Text Categorization

Predicting the Geometry of Metal Binding Sites from Protein Sequence

Performance analysis for L_2 kernel classification

Gates

Artificial Olfactory Brain for Mixture Identification

Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning

Multi-Agent Filtering with Infinitely Nested Beliefs

High-dimensional union support recovery in multivariate regression

Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG

Kernel Measures of Independence for non-iid Data

Bayesian Network Score Approximation using a Metagraph Kernel

Characterizing neural dependencies with Poisson copula models

Deep Learning with Kernel Regularization for Visual Recognition

An Online Algorithm for Maximizing Submodular Functions

Spectral Clustering with Perturbed Data

Bayesian Exponential Family PCA

Multi-resolution Exploration in Continuous Spaces

Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images

Sequential effects: Superstition or rational behavior?

Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms

A rational model of preference learning and choice prediction by children

Unsupervised Learning of Visual Sense Models for Polysemous Words

Efficient Sampling for Gaussian Process Inference using Control Variables

Clustered Multi-Task Learning: A Convex Formulation

Robust Regression and Lasso

A "Shape Aware" Model for semi-supervised Learning of Objects and its Context

Evaluating probabilities under high-dimensional latent variable models

A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning

The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction

Stochastic Relational Models for Large-scale Dyadic Data using MCMC

Learning with Consistency between Inductive Functions and Kernels

Analyzing human feature learning as nonparametric Bayesian inference

ICA based on a Smooth Estimation of the Differential Entropy

Kernelized Sorting

On the Design of Loss Functions for Classification: theory, robustness to outliers, and SavageBoost

On the Reliability of Clustering Stability in the Large Sample Regime

Breaking Audio CAPTCHAs with Machine Learning Techniques

Exact Convex Confidence-Weighted Learning

Algorithms for Infinitely Many-Armed Bandits

Improved Moves for Truncated Convex Models

Efficient Inference in Phylogenetic InDel Trees

Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation

Scalable Algorithms for String Kernels with Inexact Matching

Optimization on a Budget: A Reinforcement Learning Approach

Multi-task Gaussian Process Learning of Robot Inverse Dynamics

Near-minimax recursive density estimation on the binary hypercube

Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance

Syntactic Topic Models

Large Margin Taxonomy Embedding for Document Categorization

A general framework for investigating how far the decoding process in the brain can be simplified

Learning a discriminative hidden part model for human action recognition

Nonparametric Bayesian Learning of Switching Linear Dynamical Systems

On the Generalization Ability of Online Strongly Convex Programming Algorithms

Localized Sliced Inverse Regression

Posterior Consistency of the Silverman g-prior in Bayesian Model Choice

How memory biases affect information transmission: A rational analysis of serial reproduction

Sparse Online Learning via Truncated Gradient

Nonlinear causal discovery with additive noise models

Using matrices to model symbolic relationship

Optimal Response Initiation: Why Recent Experience Matters

On the Efficient Minimization of Classification Calibrated Surrogates

Near-optimal Regret Bounds for Reinforcement Learning

The Gaussian Process Density Sampler

Learning Transformational Invariants from Time-Varying Natural Images

Nonparametric regression and classification with joint sparsity constraints

Domain Adaptation with Multiple Sources

Deflation Methods for Sparse PCA

Robust Kernel Principal Component Analysis

Dynamic Visual Attention: Searching for coding length increments

Learning Bounded Treewidth Bayesian Networks

Mind the Duality Gap: Logarithmic regret algorithms for online optimization

A Massively Parallel Digital Learning Processor

Multi-label Multiple Kernel Learning

A Hierarchical Image Model for Polynomial-Time 2D Parsing

Adaptive Martingale Boosting

Interpreting the neural code with Formal Concept Analysis

The Infinite Factorial Hidden Markov Model

Translated Learning

Analyzing the Monotonic Feature Abstraction for Text Classification

Mixed Membership Stochastic Blockmodels

An Algorithm for Microchip Spike Sorting