NeurIPS(NIPS) 2009 论文列表
Advances in Neural Information Processing Systems 21, Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 8-11, 2008.
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Stochastic Relational Models for Large-scale Dyadic Data using MCMC.
Recursive Segmentation and Recognition Templates for 2D Parsing.
Partially Observed Maximum Entropy Discrimination Markov Networks.
Posterior Consistency of the Silverman g-prior in Bayesian Model Choice.
Hierarchical Fisher Kernels for Longitudinal Data.
Cyclizing Clusters via Zeta Function of a Graph.
Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text.
Kernel Measures of Independence for non-iid Data.
Multi-stage Convex Relaxation for Learning with Sparse Regularization.
Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models.
Fast Computation of Posterior Mode in Multi-Level Hierarchical Models.
Multi-Agent Filtering with Infinitely Nested Beliefs.
Variational Mixture of Gaussian Process Experts.
Deep Learning with Kernel Regularization for Visual Recognition.
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity.
Sequential effects: Superstition or rational behavior?
Spike Feature Extraction Using Informative Samples.
Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization.
Learning with Consistency between Inductive Functions and Kernels.
Supervised Bipartite Graph Inference.
Bayesian Network Score Approximation using a Metagraph Kernel.
An Extended Level Method for Efficient Multiple Kernel Learning.
Short-Term Depression in VLSI Stochastic Synapse.
How memory biases affect information transmission: A rational analysis of serial reproduction.
Robust Regression and Lasso.
Model selection and velocity estimation using novel priors for motion patterns.
Localized Sliced Inverse Regression.
Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG.
Dependence of Orientation Tuning on Recurrent Excitation and Inhibition in a Network Model of V1.
MAS: a multiplicative approximation scheme for probabilistic inference.
Spectral Hashing.
Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree.
Large Margin Taxonomy Embedding for Document Categorization.
Algorithms for Infinitely Many-Armed Bandits.
Learning a discriminative hidden part model for human action recognition.
Diffeomorphic Dimensionality Reduction.
Multi-Level Active Prediction of Useful Image Annotations for Recognition.
The Infinite Factorial Hidden Markov Model.
Learning to Use Working Memory in Partially Observable Environments through Dopaminergic Reinforcement.
Efficient Sampling for Gaussian Process Inference using Control Variables.
Bayesian Kernel Shaping for Learning Control.
Integrating Locally Learned Causal Structures with Overlapping Variables.
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data.
Bounding Performance Loss in Approximate MDP Homomorphisms.
Playing Pinball with non-invasive BCI.
Correlated Bigram LSA for Unsupervised Language Model Adaptation.
Breaking Audio CAPTCHAs.
Simple Local Models for Complex Dynamical Systems.
A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation.
The Recurrent Temporal Restricted Boltzmann Machine.
Using matrices to model symbolic relationship.
Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes.
An Online Algorithm for Maximizing Submodular Functions.
Sparsity of SVMs that use the epsilon-insensitive loss.
Non-parametric Regression Between Manifolds.
Grouping Contours Via a Related Image.
Fast Rates for Regularized Objectives.
Clusters and Coarse Partitions in LP Relaxations.
Convergence and Rate of Convergence of a Manifold-Based Dimension Reduction Algorithm.
The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction.
Unlabeled data: Now it helps, now it doesn't.
Regularized Co-Clustering with Dual Supervision.
Skill Characterization Based on Betweenness.
Kernel-ARMA for Hand Tracking and Brain-Machine interfacing During 3D Motor Control.
Relative Margin Machines.
PSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning.
On the Reliability of Clustering Stability in the Large Sample Regime.
Mind the Duality Gap: Logarithmic regret algorithms for online optimization.
Risk Bounds for Randomized Sample Compressed Classifiers.
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences.
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis.
On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing.
Efficient Exact Inference in Planar Ising Models.
Generative versus discriminative training of RBMs for classification of fMRI images.
Regularized Learning with Networks of Features.
Unsupervised Learning of Visual Sense Models for Polysemous Words.
Optimization on a Budget: A Reinforcement Learning Approach.
The Mondrian Process.
Non-stationary dynamic Bayesian networks.
Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms.
Temporal Dynamics of Cognitive Control.
Bayesian Model of Behaviour in Economic Games.
Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images.
Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of l1-regularized MLE.
The Infinite Hierarchical Factor Regression Model.
Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning.
Near-minimax recursive density estimation on the binary hypercube.
A mixture model for the evolution of gene expression in non-homogeneous datasets.
Kernelized Sorting.
Global Ranking Using Continuous Conditional Random Fields.
Cell Assemblies in Large Sparse Inhibitory Networks of Biologically Realistic Spiking Neurons.
Biasing Approximate Dynamic Programming with a Lower Discount Factor.
Estimation of Information Theoretic Measures for Continuous Random Variables.
Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets.
Improving on Expectation Propagation.
Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex.
A general framework for investigating how far the decoding process in the brain can be simplified.
High-dimensional support union recovery in multivariate regression.
Multi-resolution Exploration in Continuous Spaces.
On the Efficient Minimization of Classification Calibrated Surrogates.
Local Gaussian Process Regression for Real Time Online Model Learning.
Robust Kernel Principal Component Analysis.
Fitted Q-iteration by Advantage Weighted Regression.
Hebbian Learning of Bayes Optimal Decisions.
Phase transitions for high-dimensional joint support recovery.
Characterizing response behavior in multisensory perception with conflicting cues.
Implicit Mixtures of Restricted Boltzmann Machines.
Evaluating probabilities under high-dimensional latent variable models.
Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation.
Artificial Olfactory Brain for Mixture Identification.
Automatic online tuning for fast Gaussian summation.
Bounds on marginal probability distributions.
Rademacher Complexity Bounds for Non-I.I.D. Processes.
Bayesian Exponential Family PCA.
A Scalable Hierarchical Distributed Language Model.
Gates.
MDPs with Non-Deterministic Policies.
Robust Near-Isometric Matching via Structured Learning of Graphical Models.
On the Design of Loss Functions for Classification: theory, robustness to outliers, and SavageBoost.
Domain Adaptation with Multiple Sources.
Supervised Dictionary Learning.
Influence of graph construction on graph-based clustering measures.
Deflation Methods for Sparse PCA.
Reducing statistical dependencies in natural signals using radial Gaussianization.
Stress, noradrenaline, and realistic prediction of mouse behaviour using reinforcement learning.
A computational model of hippocampal function in trace conditioning.
A rational model of preference learning and choice prediction by children.
Adaptive Martingale Boosting.
Nonparametric regression and classification with joint sparsity constraints.
Dimensionality Reduction for Data in Multiple Feature Representations.
One sketch for all: Theory and Application of Conditional Random Sampling.
Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds.
Modeling the effects of memory on human online sentence processing with particle filters.
Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method.
Adaptive Template Matching with Shift-Invariant Semi-NMF.
Multiscale Random Fields with Application to Contour Grouping.
Sparse Online Learning via Truncated Gradient.
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification.
Improved Moves for Truncated Convex Models.
Scalable Algorithms for String Kernels with Inexact Matching.
Counting Solution Clusters in Graph Coloring Problems Using Belief Propagation.
Clustering via LP-based Stabilities.
On the asymptotic equivalence between differential Hebbian and temporal difference learning using a local third factor.
Policy Search for Motor Primitives in Robotics.
MCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features.
Performance analysis for L_2 kernel classification.
An ideal observer model of infant object perception.
Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM.
Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection.
On the Generalization Ability of Online Strongly Convex Programming Algorithms.
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization.
Optimal Response Initiation: Why Recent Experience Matters.
Multi-label Multiple Kernel Learning.
Natural Image Denoising with Convolutional Networks.
Online Metric Learning and Fast Similarity Search.
Inferring rankings under constrained sensing.
Clustered Multi-Task Learning: A Convex Formulation.
Continuously-adaptive discretization for message-passing algorithms.
Psychiatry: Insights into depression through normative decision-making models.
Theory of matching pursuit.
Bio-inspired Real Time Sensory Map Realignment in a Robotic Barn Owl.
Spectral Clustering with Perturbed Data.
Structured ranking learning using cumulative distribution networks.
Nonlinear causal discovery with additive noise models.
Dynamic visual attention: searching for coding length increments.
QUIC-SVD: Fast SVD Using Cosine Trees.
Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance.
Fast Prediction on a Tree.
Online Prediction on Large Diameter Graphs.
Cascaded Classification Models: Combining Models for Holistic Scene Understanding.
Shape-Based Object Localization for Descriptive Classification.
Learning Hybrid Models for Image Annotation with Partially Labeled Data.
Estimating vector fields using sparse basis field expansions.
Kernel Change-point Analysis.
Extended Grassmann Kernels for Subspace-Based Learning.
Unifying the Sensory and Motor Components of Sensorimotor Adaptation.
An improved estimator of Variance Explained in the presence of noise.
A "Shape Aware" Model for semi-supervised Learning of Objects and its Context.
Supervised Exponential Family Principal Component Analysis via Convex Optimization.
Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing.
Modeling human function learning with Gaussian processes.
Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks.
Support Vector Machines with a Reject Option.
A Massively Parallel Digital Learning Processor.
An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering.
Self-organization using synaptic plasticity.
Predictive Indexing for Fast Search.
Dependent Dirichlet Process Spike Sorting.
An Homotopy Algorithm for the Lasso with Online Observations.
Tracking Changing Stimuli in Continuous Attractor Neural Networks.
Characteristic Kernels on Groups and Semigroups.
Predicting the Geometry of Metal Binding Sites from Protein Sequence.
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems.
Resolution Limits of Sparse Coding in High Dimensions.
Regularized Policy Iteration.
ICA based on a Smooth Estimation of the Differential Entropy.
Interpreting the neural code with Formal Concept Analysis.
Learning Bounded Treewidth Bayesian Networks.
A Convex Upper Bound on the Log-Partition Function for Binary Distributions.
Generative and Discriminative Learning with Unknown Labeling Bias.
Look Ma, No Hands: Analyzing the Monotonic Feature Abstraction for Text Classification.
Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation.
From Online to Batch Learning with Cutoff-Averaging.
Load and Attentional Bayes.
Adapting to a Market Shock: Optimal Sequential Market-Making.
Translated Learning: Transfer Learning across Different Feature Spaces.
Exact Convex Confidence-Weighted Learning.
Particle Filter-based Policy Gradient in POMDPs.
Estimating Robust Query Models with Convex Optimization.
Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction.
Overlaying classifiers: a practical approach for optimal ranking.
Empirical performance maximization for linear rank statistics.
Using Bayesian Dynamical Systems for Motion Template Libraries.
Privacy-preserving logistic regression.
Tighter Bounds for Structured Estimation.
Mortal Multi-Armed Bandits.
Multi-task Gaussian Process Learning of Robot Inverse Dynamics.
Sparse Signal Recovery Using Markov Random Fields.
Linear Classification and Selective Sampling Under Low Noise Conditions.
Human Active Learning.
An interior-point stochastic approximation method and an L1-regularized delta rule.
Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform.
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes.
Learning Transformational Invariants from Natural Movies.
Online Optimization in X-Armed Bandits.
A spatially varying two-sample recombinant coalescent, with applications to HIV escape response.
Syntactic Topic Models.
Efficient Inference in Phylogenetic InDel Trees.
Goal-directed decision making in prefrontal cortex: a computational framework.
Bayesian Synchronous Grammar Induction.
Learning Taxonomies by Dependence Maximization.
Transfer Learning by Distribution Matching for Targeted Advertising.
On Bootstrapping the ROC Curve.
Characterizing neural dependencies with copula models.
Measures of Clustering Quality: A Working Set of Axioms for Clustering.
Differentiable Sparse Coding.
Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning.
Analyzing human feature learning as nonparametric Bayesian inference.
Near-optimal Regret Bounds for Reinforcement Learning.
Asynchronous Distributed Learning of Topic Models.
Sparse probabilistic projections.
A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning.
Sparse Convolved Gaussian Processes for Multi-output Regression.
Probabilistic detection of short events, with application to critical care monitoring.
Nonrigid Structure from Motion in Trajectory Space.
Mixed Membership Stochastic Blockmodels.
Reconciling Real Scores with Binary Comparisons: A New Logistic Based Model for Ranking.
Online Models for Content Optimization.
The Gaussian Process Density Sampler.
Structure Learning in Human Sequential Decision-Making.
