NeurIPS(NIPS) 2008 论文列表Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3-6, 2007. |
Regret Minimization in Games with Incomplete Information.
Predictive Matrix-Variate t Models.
Compressed Regression.
A General Boosting Method and its Application to Learning Ranking Functions for Web Search.
HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation.
Multiple-Instance Pruning For Learning Efficient Cascade Detectors.
The Noisy-Logical Distribution and its Application to Causal Inference.
Bayesian Co-Training.
Gaussian Process Models for Link Analysis and Transfer Learning.
Discriminative K-means for Clustering.
Efficient Convex Relaxation for Transductive Support Vector Machine.
Classification via Minimum Incremental Coding Length (MICL).
A New View of Automatic Relevance Determination.
Exponential Family Predictive Representations of State.
Modelling motion primitives and their timing in biologically executed movements.
Infinite State Bayes-Nets for Structured Domains.
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking .
Boosting Algorithms for Maximizing the Soft Margin.
Spatial Latent Dirichlet Allocation.
Stable Dual Dynamic Programming.
Learning with Transformation Invariant Kernels.
Scene Segmentation with CRFs Learned from Partially Labeled Images.
Modeling Natural Sounds with Modulation Cascade Processes.
Estimating disparity with confidence from energy neurons.
Configuration Estimates Improve Pedestrian Finding.
A Bayesian LDA-based model for semi-supervised part-of-speech tagging.
The Infinite Gamma-Poisson Feature Model.
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs.
Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning.
Gerald Tesauro Rajarshi Das Hoi Y. Chan Jeffrey O. Kephart David Levine Freeman L. Rawson III Charles Lefurgy
Convex Learning with Invariances.
Collapsed Variational Inference for HDP.
Bayesian Agglomerative Clustering with Coalescents.
Receding Horizon Differential Dynamic Programming.
Hierarchical Penalization.
A Game-Theoretic Approach to Apprenticeship Learning.
Efficient Bayesian Inference for Dynamically Changing Graphs.
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation.
Loop Series and Bethe Variational Bounds in Attractive Graphical Models.
Online Linear Regression and Its Application to Model-Based Reinforcement Learning.
A Bayesian Model of Conditioned Perception.
An in-silico Neural Model of Dynamic Routing through Neuronal Coherence.
New Outer Bounds on the Marginal Polytope.
Colored Maximum Variance Unfolding.
Bundle Methods for Machine Learning.
An Analysis of Inference with the Universum.
The Value of Labeled and Unlabeled Examples when the Model is Imperfect.
Ensemble Clustering using Semidefinite Programming.
Hidden Common Cause Relations in Relational Learning.
Combined discriminative and generative articulated pose and non-rigid shape estimation.
A Constraint Generation Approach to Learning Stable Linear Dynamical Systems.
Collective Inference on Markov Models for Modeling Bird Migration.
Sparse Overcomplete Latent Variable Decomposition of Counts Data.
Better than least squares: comparison of objective functions for estimating linear-nonlinear models.
Cluster Stability for Finite Samples.
Multiple-Instance Active Learning.
Message Passing for Max-weight Independent Set.
Linear programming analysis of loopy belief propagation for weighted matching.
Markov Chain Monte Carlo with People.
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes.
Probabilistic Matrix Factorization.
Object Recognition by Scene Alignment.
Topmoumoute Online Natural Gradient Algorithm.
Learning the 2-D Topology of Images.
Theoretical Analysis of Heuristic Search Methods for Online POMDPs.
Bayes-Adaptive POMDPs.
GRIFT: A graphical model for inferring visual classification features from human data.
On Ranking in Survival Analysis: Bounds on the Concordance Index.
SpAM: Sparse Additive Models.
Retrieved context and the discovery of semantic structure.
Sparse Feature Learning for Deep Belief Networks.
Random Features for Large-Scale Kernel Machines.
Fast Variational Inference for Large-scale Internet Diagnosis.
Neural characterization in partially observed populations of spiking neurons.
Discriminative Log-Linear Grammars with Latent Variables.
Congruence between model and human attention reveals unique signatures of critical visual events.
A Risk Minimization Principle for a Class of Parzen Estimators.
Kernels on Attributed Pointsets with Applications.
Modeling image patches with a directed hierarchy of Markov random fields.
CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation.
Variational inference for Markov jump processes.
Heterogeneous Component Analysis.
Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization.
Distributed Inference for Latent Dirichlet Allocation.
Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons.
The Generalized FITC Approximation.
Robust Regression with Twinned Gaussian Processes.
Continuous Time Particle Filtering for fMRI.
An Analysis of Convex Relaxations for MAP Estimation.
Experience-Guided Search: A Theory of Attentional Control.
Stability Bounds for Non-i.i.d. Processes.
The Infinite Markov Model.
Learning to classify complex patterns using a VLSI network of spiking neurons.
Locality and low-dimensions in the prediction of natural experience from fMRI.
A neural network implementing optimal state estimation based on dynamic spike train decoding.
Scan Strategies for Meteorological Radars.
Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations.
Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition.
Receptive Fields without Spike-Triggering.
Consistent Minimization of Clustering Objective Functions.
Support Vector Machine Classification with Indefinite Kernels.
People Tracking with the Laplacian Eigenmaps Latent Variable Model.
Boosting the Area under the ROC Curve.
Semi-Supervised Multitask Learning.
Mining Internet-Scale Software Repositories.
Blind channel identification for speech dereverberation using l1-norm sparse learning.
Agreement-Based Learning.
McRank: Learning to Rank Using Multiple Classification and Gradient Boosting.
A Unified Near-Optimal Estimator For Dimension Reduction in lalpha(0 < alpha <= 2) Using Stable Random Projections.
Hippocampal Contributions to Control: The Third Way.
Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity.
Sparse deep belief net model for visual area V2.
Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains.
Non-parametric Modeling of Partially Ranked Data.
Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods.
Convex Clustering with Exemplar-Based Models.
The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information.
Extending position/phase-shift tuning to motion energy neurons improves velocity discrimination.
Statistical Analysis of Semi-Supervised Regression.
A Randomized Algorithm for Large Scale Support Vector Learning.
Structured Learning with Approximate Inference.
Selecting Observations against Adversarial Objectives.
Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion.
Learning with Tree-Averaged Densities and Distributions.
Learning and using relational theories.
Privacy-Preserving Belief Propagation and Sampling.
Multi-Task Learning via Conic Programming.
Local Algorithms for Approximate Inference in Minor-Excluded Graphs.
Computing Robust Counter-Strategies.
Density Estimation under Independent Similarly Distributed Sampling Assumptions.
Temporal Difference Updating without a Learning Rate.
Efficient Inference for Distributions on Permutations.
What makes some POMDP problems easy to approximate?
Learning Monotonic Transformations for Classification.
Ultrafast Monte Carlo for Statistical Summations.
Bayesian Policy Learning with Trans-Dimensional MCMC.
Modeling homophily and stochastic equivalence in symmetric relational data.
Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach.
Random Projections for Manifold Learning.
Nearest-Neighbor-Based Active Learning for Rare Category Detection.
Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria.
Catching Change-points with Lasso.
Testing for Homogeneity with Kernel Fisher Discriminant Analysis.
Convex Relaxations of Latent Variable Training.
Discriminative Batch Mode Active Learning.
A Kernel Statistical Test of Independence.
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks.
Expectation Maximization and Posterior Constraints.
Competition Adds Complexity.
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations.
A configurable analog VLSI neural network with spiking neurons and self-regulating plastic synapses .
Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression.
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior.
On higher-order perceptron algorithms.
Iterative Non-linear Dimensionality Reduction with Manifold Sculpting.
Learning Horizontal Connections in a Sparse Coding Model of Natural Images.
The discriminant center-surround hypothesis for bottom-up saliency.
Kernel Measures of Conditional Dependence.
Discovering Weakly-Interacting Factors in a Complex Stochastic Process.
Learning the structure of manifolds using random projections.
Sequential Hypothesis Testing under Stochastic Deadlines.
A Bayesian Framework for Cross-Situational Word-Learning.
Optimal models of sound localization by barn owls.
EEG-Based Brain-Computer Interaction: Improved Accuracy by Automatic Single-Trial Error Detection.
Learning Visual Attributes.
Anytime Induction of Cost-sensitive Trees.
Catching Up Faster in Bayesian Model Selection and Model Averaging.
Active Preference Learning with Discrete Choice Data.
A probabilistic model for generating realistic lip movements from speech.
Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms.
Automatic Generation of Social Tags for Music Recommendation.
Efficient multiple hyperparameter learning for log-linear models.
The rat as particle filter.
Measuring Neural Synchrony by Message Passing.
A general agnostic active learning algorithm.
The Price of Bandit Information for Online Optimization.
TrueSkill Through Time: Revisiting the History of Chess.
Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes.
An online Hebbian learning rule that performs Independent Component Analysis.
Second Order Bilinear Discriminant Analysis for single trial EEG analysis.
How SVMs can estimate quantiles and the median.
Cooled and Relaxed Survey Propagation for MRFs.
Rapid Inference on a Novel AND/OR graph for Object Detection, Segmentation and Parsing.
Regularized Boost for Semi-Supervised Learning.
Efficient Principled Learning of Thin Junction Trees.
Augmented Functional Time Series Representation and Forecasting with Gaussian Processes.
Parallelizing Support Vector Machines on Distributed Computers.
Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis.
Predicting human gaze using low-level saliency combined with face detection.
A learning framework for nearest neighbor search.
Subspace-Based Face Recognition in Analog VLSI.
Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks.
Discriminative Keyword Selection Using Support Vector Machines.
The Distribution Family of Similarity Distances.
Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons.
FilterBoost: Regression and Classification on Large Datasets.
Unsupervised Feature Selection for Accurate Recommendation of High-Dimensional Image Data.
A Probabilistic Approach to Language Change.
The Tradeoffs of Large Scale Learning.
Multi-task Gaussian Process Prediction.
Feature Selection Methods for Improving Protein Structure Prediction with Rosetta.
Learning Bounds for Domain Adaptation.
Supervised Topic Models.
Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing.
Benjamin Blankertz Motoaki Kawanabe Ryota Tomioka Friederike U. Hohlefeld Vadim V. Nikulin Klaus-Robert Müller
Incremental Natural Actor-Critic Algorithms.
Near-Maximum Entropy Models for Binary Neural Representations of Natural Images.
On Sparsity and Overcompleteness in Image Models.
Comparing Bayesian models for multisensory cue combination without mandatory integration.
One-Pass Boosting.
Adaptive Online Gradient Descent.
Optimal ROC Curve for a Combination of Classifiers.
DIFFRAC: a discriminative and flexible framework for clustering.
Progressive mixture rules are deviation suboptimal.
Random Sampling of States in Dynamic Programming.
A Spectral Regularization Framework for Multi-Task Structure Learning.
Variational Inference for Diffusion Processes.
Fitted Q-iteration in continuous action-space MDPs.
Inferring Elapsed Time from Stochastic Neural Processes.