NeurIPS(NIPS) 2011 论文列表Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain. |
Accelerated Adaptive Markov Chain for Partition Function Computation.
Exploiting spatial overlap to efficiently compute appearance distances between image windows.
High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity.
Inference in continuous-time change-point models.
Kernel Embeddings of Latent Tree Graphical Models.
Predicting response time and error rates in visual search.
Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation.
Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound.
Hashing Algorithms for Large-Scale Learning.
EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning.
Demixed Principal Component Analysis.
On the Universality of Online Mirror Descent.
Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning.
Selecting the State-Representation in Reinforcement Learning.
Select and Sample - A Model of Efficient Neural Inference and Learning.
Hierarchically Supervised Latent Dirichlet Allocation.
A Collaborative Mechanism for Crowdsourcing Prediction Problems.
Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints.
Inferring spike-timing-dependent plasticity from spike train data.
A reinterpretation of the policy oscillation phenomenon in approximate policy iteration.
Query-Aware MCMC.
Neural Reconstruction with Approximate Message Passing (NeuRAMP).
Algorithms for Hyper-Parameter Optimization.
Convergent Fitted Value Iteration with Linear Function Approximation.
Selecting Receptive Fields in Deep Networks.
Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels.
Variational Gaussian Process Dynamical Systems.
On Tracking The Partition Function.
Continuous-Time Regression Models for Longitudinal Networks.
Linear Submodular Bandits and their Application to Diversified Retrieval.
Gaussian process modulated renewal processes.
Autonomous Learning of Action Models for Planning.
Co-Training for Domain Adaptation.
Contextual Gaussian Process Bandit Optimization.
The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning.
Understanding the Intrinsic Memorability of Images.
Regularized Laplacian Estimation and Fast Eigenvector Approximation.
Speedy Q-Learning.
TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning.
Quasi-Newton Methods for Markov Chain Monte Carlo.
A rational model of causal inference with continuous causes.
Fast and Accurate k-means For Large Datasets.
Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts.
Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability.
Practical Variational Inference for Neural Networks.
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning.
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation.
Testing a Bayesian Measure of Representativeness Using a Large Image Database.
Improved Algorithms for Linear Stochastic Bandits.
Analytical Results for the Error in Filtering of Gaussian Processes.
The Manifold Tangent Classifier.
Sparse Features for PCA-Like Linear Regression.
Evaluating the inverse decision-making approach to preference learning.
Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation.
Blending Autonomous Exploration and Apprenticeship Learning.
An Empirical Evaluation of Thompson Sampling.
Active Ranking using Pairwise Comparisons.
Randomized Algorithms for Comparison-based Search.
Multi-Bandit Best Arm Identification.
A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm.
Julie Dethier Paul Nuyujukian Chris Eliasmith Terrence C. Stewart Shauki A. Elasaad Krishna V. Shenoy Kwabena Boahen
Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss.
Statistical Tests for Optimization Efficiency.
Trace Lasso: a trace norm regularization for correlated designs.
Generalizing from Several Related Classification Tasks to a New Unlabeled Sample.
The Fixed Points of Off-Policy TD.
Nearest Neighbor based Greedy Coordinate Descent.
Generalised Coupled Tensor Factorisation.
Scalable Training of Mixture Models via Coresets.
Learning with the weighted trace-norm under arbitrary sampling distributions.
Multiclass Boosting: Theory and Algorithms.
Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms.
Prismatic Algorithm for Discrete D.C. Programming Problem.
Confidence Sets for Network Structure.
PiCoDes: Learning a Compact Code for Novel-Category Recognition.
Active Learning with a Drifting Distribution.
Anatomically Constrained Decoding of Finger Flexion from Electrocorticographic Signals.
Learning to Learn with Compound HD Models.
Priors over Recurrent Continuous Time Processes.
Active learning of neural response functions with Gaussian processes.
How biased are maximum entropy models?
Spectral Methods for Learning Multivariate Latent Tree Structure.
Sparse Estimation with Structured Dictionaries.
Predicting Dynamic Difficulty.
Similarity-based Learning via Data Driven Embeddings.
MAP Inference for Bayesian Inverse Reinforcement Learning.
The Doubly Correlated Nonparametric Topic Model.
Unsupervised learning models of primary cortical receptive fields and receptive field plasticity.
A Model for Temporal Dependencies in Event Streams.
Iterative Learning for Reliable Crowdsourcing Systems.
Policy Gradient Coagent Networks.
On Learning Discrete Graphical Models using Greedy Methods.
Sparse recovery by thresholded non-negative least squares.
Efficient Offline Communication Policies for Factored Multiagent POMDPs.
Variance Penalizing AdaBoost.
Learning a Distance Metric from a Network.
A concave regularization technique for sparse mixture models.
Robust Lasso with missing and grossly corrupted observations.
Structural equations and divisive normalization for energy-dependent component analysis.
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions.
An Application of Tree-Structured Expectation Propagation for Channel Decoding.
Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors.
Variance Reduction in Monte-Carlo Tree Search.
Probabilistic Joint Image Segmentation and Labeling.
Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery.
Ranking annotators for crowdsourced labeling tasks.
Bayesian Bias Mitigation for Crowdsourcing.
An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments.
Sparse Recovery with Brownian Sensing.
Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint.
Online Learning: Stochastic, Constrained, and Smoothed Adversaries.
Sparse Bayesian Multi-Task Learning.
Transfer from Multiple MDPs.
Kernel Bayes' Rule.
Advice Refinement in Knowledge-Based SVMs.
A Non-Parametric Approach to Dynamic Programming.
Learning to Search Efficiently in High Dimensions.
Non-conjugate Variational Message Passing for Multinomial and Binary Regression.
Bayesian Spike-Triggered Covariance Analysis.
PAC-Bayesian Analysis of Contextual Bandits.
Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs.
Agnostic Selective Classification.
Adaptive Hedge.
Better Mini-Batch Algorithms via Accelerated Gradient Methods.
Universal low-rank matrix recovery from Pauli measurements.
Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines.
Infinite Latent SVM for Classification and Multi-task Learning.
Learning Anchor Planes for Classification.
Multi-armed bandits on implicit metric spaces.
The Fast Convergence of Boosting.
See the Tree Through the Lines: The Shazoo Algorithm.
Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning.
Energetically Optimal Action Potentials.
Committing Bandits.
Learning Auto-regressive Models from Sequence and Non-sequence Data.
Optimal learning rates for least squares SVMs using Gaussian kernels.
Higher-Order Correlation Clustering for Image Segmentation.
Uniqueness of Belief Propagation on Signed Graphs.
Submodular Multi-Label Learning.
Learning person-object interactions for action recognition in still images.
t-divergence Based Approximate Inference.
Pylon Model for Semantic Segmentation.
Spatial distance dependent Chinese restaurant processes for image segmentation.
Joint 3D Estimation of Objects and Scene Layout.
Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization.
How Do Humans Teach: On Curriculum Learning and Teaching Dimension.
A blind sparse deconvolution method for neural spike identification.
Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis.
Sequence learning with hidden units in spiking neural networks.
Co-regularized Multi-view Spectral Clustering.
An Exact Algorithm for F-Measure Maximization.
Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices.
On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference.
From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models.
Bayesian Partitioning of Large-Scale Distance Data.
Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities.
Empirical models of spiking in neural populations.
Gaussian Process Training with Input Noise.
Boosting with Maximum Adaptive Sampling.
Portmanteau Vocabularies for Multi-Cue Image Representation.
Algorithms and hardness results for parallel large margin learning.
Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories.
Structured Learning for Cell Tracking.
Monte Carlo Value Iteration with Macro-Actions.
Finite Time Analysis of Stratified Sampling for Monte Carlo.
Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach.
Qibin Zhao Cesar F. Caiafa Danilo P. Mandic Liqing Zhang Tonio Ball Andreas Schulze-Bonhage Andrzej Cichocki
On the accuracy of l1-filtering of signals with block-sparse structure.
Large-Scale Category Structure Aware Image Categorization.
Greedy Model Averaging.
Beating SGD: Learning SVMs in Sublinear Time.
Composite Multiclass Losses.
Orthogonal Matching Pursuit with Replacement.
A More Powerful Two-Sample Test in High Dimensions using Random Projection.
Message-Passing for Approximate MAP Inference with Latent Variables.
Active dendrites: adaptation to spike-based communication.
ShareBoost: Efficient multiclass learning with feature sharing.
Metric Learning with Multiple Kernels.
Collective Graphical Models.
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference.
Im2Text: Describing Images Using 1 Million Captioned Photographs.
Divide-and-Conquer Matrix Factorization.
Sparse Filtering.
Structured sparse coding via lateral inhibition.
Online Submodular Set Cover, Ranking, and Repeated Active Learning.
Budgeted Optimization with Concurrent Stochastic-Duration Experiments.
SpaRCS: Recovering low-rank and sparse matrices from compressive measurements.
Approximating Semidefinite Programs in Sublinear Time.
Group Anomaly Detection using Flexible Genre Models.
Active Classification based on Value of Classifier.
Inverting Grice's Maxims to Learn Rules from Natural Language Extractions.
Shahed Sorower Thomas G. Dietterich Janardhan Rao Doppa John Walker Orr Prasad Tadepalli Xiaoli Z. Fern
Structure Learning for Optimization.
Stochastic convex optimization with bandit feedback.
Lower Bounds for Passive and Active Learning.
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning.
Complexity of Inference in Latent Dirichlet Allocation.
Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons.
Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators.
Probabilistic amplitude and frequency demodulation.
Statistical Performance of Convex Tensor Decomposition.
The Kernel Beta Process.
Noise Thresholds for Spectral Clustering.
Learning Eigenvectors for Free.
On the Analysis of Multi-Channel Neural Spike Data.
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression.
Maximum Covariance Unfolding : Manifold Learning for Bimodal Data.
Minimax Localization of Structural Information in Large Noisy Matrices.
Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries.
Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction.
Greedy Algorithms for Structurally Constrained High Dimensional Problems.
Distributed Delayed Stochastic Optimization.
Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning.
Selective Prediction of Financial Trends with Hidden Markov Models.
Information Rates and Optimal Decoding in Large Neural Populations.
Data Skeletonization via Reeb Graphs.
Prediction strategies without loss.
Modelling Genetic Variations using Fragmentation-Coagulation Processes.
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity.
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection.
Reconstructing Patterns of Information Diffusion from Incomplete Observations.
Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness.
Probabilistic Modeling of Dependencies Among Visual Short-Term Memory Representations.
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance.
Dynamical segmentation of single trials from population neural data.
Biljana Petreska Byron M. Yu John P. Cunningham Gopal Santhanam Stephen I. Ryu Krishna V. Shenoy Maneesh Sahani
A Machine Learning Approach to Predict Chemical Reactions.
Learning unbelievable probabilities.
k-NN Regression Adapts to Local Intrinsic Dimension.
Reinforcement Learning using Kernel-Based Stochastic Factorization.
Why The Brain Separates Face Recognition From Object Recognition.
Clustered Multi-Task Learning Via Alternating Structure Optimization.
Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent.
From Bandits to Experts: On the Value of Side-Observations.
Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment.
Shallow vs. Deep Sum-Product Networks.
A Convergence Analysis of Log-Linear Training.
Learning to Agglomerate Superpixel Hierarchies.
Viren Jain Srinivas C. Turaga Kevin L. Briggman Moritz Helmstaedter Winfried Denk H. Sebastian Seung
On Causal Discovery with Cyclic Additive Noise Models.
Efficient inference in matrix-variate Gaussian models with \iid observation noise.
Learning a Tree of Metrics with Disjoint Visual Features.
Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation.
Solving Decision Problems with Limited Information.
Relative Density-Ratio Estimation for Robust Distribution Comparison.
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers.
Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification.
Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition.
Crowdclustering.
$\theta$-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding.
Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC.
Large-Scale Sparse Principal Component Analysis with Application to Text Data.
Generalized Beta Mixtures of Gaussians.
An ideal observer model for identifying the reference frame of objects.
A Two-Stage Weighting Framework for Multi-Source Domain Adaptation.
Improving Topic Coherence with Regularized Topic Models.
Projection onto A Nonnegative Max-Heap.
Efficient anomaly detection using bipartite k-NN graphs.
Emergence of Multiplication in a Biophysical Model of a Wide-Field Visual Neuron for Computing Object Approaches: Dynamics, Peaks, & Fits.
On fast approximate submodular minimization.
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning.
Object Detection with Grammar Models.
Semi-supervised Regression via Parallel Field Regularization.
History distribution matching method for predicting effectiveness of HIV combination therapies.
Expressive Power and Approximation Errors of Restricted Boltzmann Machines.
RTRMC: A Riemannian trust-region method for low-rank matrix completion.
Heavy-tailed Distances for Gradient Based Image Descriptors.
Convergent Bounds on the Euclidean Distance.
Phase transition in the family of p-resistances.
Multiple Instance Filtering.
Differentially Private M-Estimators.
Efficient Methods for Overlapping Group Lasso.
Efficient Online Learning via Randomized Rounding.
A Denoising View of Matrix Completion.
Optimal Reinforcement Learning for Gaussian Systems.
Inductive reasoning about chimeric creatures.
Thinning Measurement Models and Questionnaire Design.
Extracting Speaker-Specific Information with a Regularized Siamese Deep Network.
Maximum Margin Multi-Label Structured Prediction.
Robust Multi-Class Gaussian Process Classification.
Dimensionality Reduction Using the Sparse Linear Model.
Analysis and Improvement of Policy Gradient Estimation.
Learning Higher-Order Graph Structure with Features by Structure Penalty.
Semantic Labeling of 3D Point Clouds for Indoor Scenes.
Inferring Interaction Networks using the IBP applied to microRNA Target Prediction.
Additive Gaussian Processes.
Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron.
Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent.
Multi-View Learning of Word Embeddings via CCA.
Matrix Completion for Multi-label Image Classification.
Generalized Lasso based Approximation of Sparse Coding for Visual Recognition.
Action-Gap Phenomenon in Reinforcement Learning.
A Global Structural EM Algorithm for a Model of Cancer Progression.
Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds.
Multiple Instance Learning on Structured Data.
Variational Learning for Recurrent Spiking Networks.
Environmental statistics and the trade-off between model-based and TD learning in humans.
Transfer Learning by Borrowing Examples for Multiclass Object Detection.
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials.
On Strategy Stitching in Large Extensive Form Multiplayer Games.
Learning large-margin halfspaces with more malicious noise.
A Reinforcement Learning Theory for Homeostatic Regulation.
Image Parsing with Stochastic Scene Grammar.
Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction.
Sparse Manifold Clustering and Embedding.
Penalty Decomposition Methods for Rank Minimization.
On U-processes and clustering performance.
Video Annotation and Tracking with Active Learning.
Nonlinear Inverse Reinforcement Learning with Gaussian Processes.
Shaping Level Sets with Submodular Functions.
Maximum Margin Multi-Instance Learning.