NIPS 2011 Papers

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t-divergence Based Approximate Inference

Data Skeletonization via Reeb Graphs

Demixed Principal Component Analysis

Learning sparse inverse covariance matrices in the presence of confounders

Inferring spike-timing-dependent plasticity from spike train data

An Exact Algorithm for F-Measure Maximization

Unfolding Recursive Autoencoders for Paraphrase Detection

Prediction strategies without loss

Confidence Sets for Network Structure

Efficient coding with a population of Linear-Nonlinear neurons

Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices

Nearest Neighbor based Greedy Coordinate Descent

Probabilistic Joint Image Segmentation and Labeling

Sparse Estimation with Structured Dictionaries

Spectral Methods for Learning Multivariate Latent Tree Structure

Sparse Features for PCA-Like Linear Regression

Priors over Recurrent Continuous Time Processes

Submodular Multi-Label Learning

Modelling Genetic Variations using Fragmentation-Coagulation Processes

Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

Multiple Instance Learning on Structured Data

Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment

On the Analysis of Multi-Channel Neural Spike Data

Learning to Agglomerate Superpixel Hierarchies

Information Rates and Optimal Decoding in Large Neural Populations

Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning

Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities

Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery

The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers

Why The Brain Separates Face Recognition From Object Recognition

Variance Penalizing AdaBoost

Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction

Video Annotation and Tracking with Active Learning

Thinning Measurement Models and Questionnaire Design

Structured sparse coding via lateral inhibition

Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning

Transfer Learning by Borrowing Examples

Universal low-rank matrix recovery from Pauli measurements

Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning

Uniqueness of Belief Propagation on Signed Graphs

Variational Gaussian Process Dynamical Systems

The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning

Variational Learning for Recurrent Spiking Networks

Structured Learning for Cell Tracking

The Fixed Points of Off-Policy TD

Variance Reduction in Monte-Carlo Tree Search

Structure Learning for Optimization

Statistical Performance of Convex Tensor Decomposition

Transfer from Multiple MDPs

Understanding the Intrinsic Memorability of Images

Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories

Unsupervised learning models of primary cortical receptive fields and receptive field plasticity

The Manifold Tangent Classifier

Sparse recovery by thresholded non-negative least squares

Robust Multi-Class Gaussian Process Classification

The Fast Convergence of Boosting

Selective Prediction of Financial Trends with Hidden Markov Models

The Kernel Beta Process

Shaping Level Sets with Submodular Functions

Shallow vs. Deep Sum-Product Networks

TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning

Speedy Q-Learning

RTRMC: A Riemannian trust-region method for low-rank matrix completion

ShareBoost: Efficient multiclass learning with feature sharing

SpaRCS: Recovering low-rank and sparse matrices from compressive measurements

Similarity-based Learning via Data Driven Embeddings

Stochastic convex optimization with bandit feedback

The Doubly Correlated Nonparametric Topic Model

Statistical Tests for Optimization Efficiency

Sparse Filtering

Sequence learning with hidden units in spiking neural networks

Spatial distance dependent Chinese Restaurant Process for image segmentation

Solving Decision Problems with Limited Information

Trace Lasso: a trace norm regularization for correlated designs

Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC

Semi-supervised Regression via Parallel Field Regularization

Sparse Bayesian Multi-Task Learning

Testing a Bayesian Measure of Representativeness Using a Large Image Database

Structural equations and divisive normalization for energy-dependent component analysis

Sparse Manifold Clustering and Embedding

Selecting Receptive Fields in Deep Networks

Active learning of neural response functions with Gaussian processes

Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification

Robust Lasso with missing and grossly corrupted observations

Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation

Select and Sample: A Model of Efficient Neural Inference and Learning

Regularized Laplacian Estimation and Fast Eigenvector Approximation

See the Tree Through the Lines: The Shazoo Algorithm

Scalable Training of Mixture Models via Coresets

Relative Density-Ratio Estimation for Robust Distribution Comparison

Selecting the State-Representation in Reinforcement Learning

Sparse Recovery with Brownian Sensing

Semantic Labeling of 3D Point Clouds for Indoor Scenes

Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound

Reconstructing Patterns of Information Diffusion from Incomplete Observations

Reinforcement Learning using Kernel-Based Stochastic Factorization

Ranking annotators for crowdsourced labeling tasks

Quasi-Newton Methods for Markov Chain Monte Carlo

Policy Gradient Coagent Networks

Projection onto A Nonnegative Max-Heap

Randomized Algorithms for Comparison-based Search

PiCoDes: Learning a Compact Code for Novel-Category Recognition

Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning

Predicting Dynamic Difficulty

Prismatic Algorithm for Discrete D.C. Programming Problem

Optimal learning rates for least squares SVMs using Gaussian kernels

Practical Variational Inference for Neural Networks

Optimal Reinforcement Learning for Gaussian Systems

Probabilistic Modeling of Dependencies Among Visual Short-Term Memory Representations

On U-processes and clustering performance

On Strategy Stitching in Large Extensive Form Multiplayer Games

Optimistic Optimization of Deterministic Functions

Query-Aware MCMC

Predicting response time and error rates in visual search

Probabilistic amplitude and frequency demodulation

Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation

Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels

On the accuracy of l1-filtering of signals with block-sparse structure

Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction

Pylon Model for Semantic Segmentation

PAC-Bayesian Analysis of Contextual Bandits

Penalty Decomposition Methods for Rank Minimization

Online Submodular Set Cover, Ranking, and Repeated Active Learning

Phase transition in the family of p-resistances

Non-conjugate Variational Message Passing for Multinomial and Binary Regression

A reinterpretation of the policy oscillation phenomenon in approximate policy iteration

On Tracking The Partition Function

Nonstandard Interpretations of Probabilistic Programs for Efficient Inference

Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability

Multi-armed bandits on implicit metric spaces

Online Learning: Stochastic, Constrained, and Smoothed Adversaries

Neural Reconstruction with Approximate Message Passing (NeuRAMP)

Message-Passing for Approximate MAP Inference with Latent Variables

Object Detection with Grammar Models

On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference

Noise Thresholds for Spectral Clustering

Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation

Multi-Bandit Best Arm Identification

Multiple Instance Filtering

Multi-View Learning of Word Embeddings via CCA

Matrix Completion for Image Classification

Portmanteau Vocabularies for Multi-Cue Image Representation

Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning

On the Universality of Online Mirror Descent

Linear Submodular Bandits and their Application to Diversified Retrieval

Maximum Margin Multi-Label Structured Prediction

Orthogonal Matching Pursuit with Replacement

MAP Inference for Bayesian Inverse Reinforcement Learning

Nonlinear Inverse Reinforcement Learning with Gaussian Processes

Monte Carlo Value Iteration with Macro-Actions

Multiclass Boosting: Theory and Algorithms

On Learning Discrete Graphical Models using Greedy Methods

Learning unbelievable marginal probabilities

Minimax Localization of Structural Information in Large Noisy Matrices

Learning with the weighted trace-norm under arbitrary sampling distributions

Lower Bounds for Passive and Active Learning

Learning to Search Efficiently in High Dimensions

Maximum Covariance Unfolding : Manifold Learning for Bimodal Data

Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries

Metric Learning with Multiple Kernels

Learning to Learn with Compound HD Models

Learning person-object interactions for action recognition in still images

Learning Higher-Order Graph Structure with Features by Structure Penalty

Learning large-margin halfspaces with more malicious noise

Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint

Large-Scale Sparse Principal Component Analysis with Application to Text Data

Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors

Learning Eigenvectors for Free

Large-Scale Category Structure Aware Image Categorization

k-NN Regression Adapts to Local Intrinsic Dimension

Learning Auto-regressive Models from Sequence and Non-sequence Data

Learning a Distance Metric from a Network

Kernel Embeddings of Latent Tree Graphical Models

ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning

Kernel Bayes' Rule

Inductive reasoning about chimeric creatures

Hierarchically Supervised Latent Dirichlet Allocation

Learning a Tree of Metrics with Disjoint Visual Features

Learning Anchor Planes for Classification

Improving Topic Coherence with Regularized Topic Models

High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity

Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation

Group Anomaly Detection using Flexible Genre Models

Infinite Latent SVM for Classification and Multi-task Learning

History distribution matching method for predicting effectiveness of HIV combination therapies

Image Parsing with Stochastic Scene Grammar

Inverting Grice's Maxims to Learn Rules from Natural Language Extractions

Higher-Order Correlation Clustering for Image Segmentation

How biased are maximum entropy models?

Inferring Interaction Networks using the IBP applied to microRNA Target Prediction

Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

How Do Humans Teach: On Curriculum Learning and Teaching Dimension

Joint 3D Estimation of Objects and Scene Layout

Heavy-tailed Distances for Gradient Based Image Descriptors

Improved Algorithms for Linear Stochastic Bandits

Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent

Iterative Learning for Reliable Crowdsourcing Systems

Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis

Hashing Algorithms for Large-Scale Learning

Greedy Algorithms for Structurally Constrained High Dimensional Problems

Inference in continuous time changepoint point models

Greedy Model Averaging

Generalized Beta Mixtures of Gaussians

Generalized Lasso based Approximation of Sparse Coding for Visual Recognition

High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions

Generalizing from Several Related Classification Tasks to a New Unlabeled Sample

Generalised Coupled Tensor Factorisation

Gaussian process modulated renewal processes

Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss

Gaussian Process Training with Input Noise

From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models

Fast and Accurate k-means For Large Datasets

Hierarchical Matching Pursuit for Recognition: Architecture and Fast Algorithms

Exploiting spatial overlap to efficiently compute appearance distances between image windows

From Bandits to Experts: On the Value of Side-Observations

EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning

Fast approximate submodular minimization

Extracting Speaker-Specific Information with a Regularized Siamese Deep Network

Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition

Environmental statistics and the trade-off between model-based and TD learning in humans

Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron

Finite Time Analysis of Stratified Sampling for Monte Carlo

Empirical models of spiking in neural populations

Expressive Power and Approximation Errors of Restricted Boltzmann Machines

Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

Emergence of Multiplication in a Biophysical Model of a Wide-Field Visual Neuron for Computing Object Approaches: Dynamics, Peaks, & Fits

Energetically Optimal Action Potentials

Efficient Methods for Overlapping Group Lasso

Efficient Online Learning via Randomized Rounding

Efficient anomaly detection using bipartite k-NN graphs

Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression

Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines

Distributed Delayed Stochastic Optimization

Evaluating computational models of preference learning

Efficient Offline Communication Policies for Factored Multiagent POMDPs

Divide-and-Conquer Matrix Factorization

Dynamical segmentation of single trials from population neural data

Convergent Fitted Value Iteration with Linear Function Approximation

Co-Training for Domain Adaptation

Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators

Differentially Private M-Estimators

Decoding of Finger Flexion from Electrocorticographic Signals Using Switching Non-Parametric Dynamic Systems

Continuous-Time Regression Models for Longitudinal Networks

Dimensionality Reduction Using the Sparse Linear Model

Crowdclustering

Convergent Bounds on the Euclidean Distance

Boosting with Maximum Adaptive Sampling

Collective Graphical Models

Beating SGD: Learning SVMs in Sublinear Time

Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization

Committing Bandits

Composite Multiclass Losses

Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs

Clustered Multi-Task Learning Via Alternating Structure Optimization

Causal Discovery with Cyclic Additive Noise Models

Better Mini-Batch Algorithms via Accelerated Gradient Methods

Contextual Gaussian Process Bandit Optimization

Complexity of Inference in Latent Dirichlet Allocation

An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments

Analytical Results for the Error in Filtering of Gaussian Processes

Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts

Budgeted Optimization with Concurrent Stochastic-Duration Experiments

Bayesian Spike-Triggered Covariance Analysis

Bayesian Bias Mitigation for Crowdsourcing

An Empirical Evaluation of Thompson Sampling

Blending Autonomous Exploration and Apprenticeship Learning

Autonomous Learning of Action Models for Planning

Analysis and Improvement of Policy Gradient Estimation

Im2Text: Describing Images Using 1 Million Captioned Photographs

An ideal observer model for identifying the reference frame of objects

Co-regularized Multi-view Spectral Clustering

Bayesian Partitioning of Large-Scale Distance Data

Approximating Semidefinite Programs in Sublinear Time

Agnostic Selective Classification

Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints

Active Ranking using Pairwise Comparisons

Advice Refinement in Knowledge-Based SVMs

Additive Gaussian Processes

Algorithms and hardness results for parallel large margin learning

Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity

Adaptive Hedge

Algorithms for Hyper-Parameter Optimization

Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds

Active Learning with a Drifting Distribution

Action-Gap Phenomenon in Reinforcement Learning

An Application of Tree-Structured Expectation Propagation for Channel Decoding

A Non-Parametric Approach to Dynamic Programming

Active dendrites: adaptation to spike-based communication

A blind sparse deconvolution method for neural spike identification

A Convergence Analysis of Log-Linear Training

Accelerated Adaptive Markov Chain for Partition Function Computation

A Collaborative Mechanism for Crowdsourcing Prediction Problems

A rational model of causal inference with continuous causes

Active Classification based on Value of Classifier

A Global Structural EM Algorithm for a Model of Cancer Progression

A More Powerful Two-Sample Test in High Dimensions using Random Projection

A Reinforcement Learning Theory for Homeostatic Regulation

A Two-Stage Weighting Framework for Multi-Source Domain Adaptation

A Maximum Margin Multi-Instance Learning Framework for Image Categorization

A Machine Learning Approach to Predict Chemical Reactions

A Denoising View of Matrix Completion

$\theta$-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding

A concave regularization technique for sparse mixture models

A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm

A Model for Temporal Dependencies in Event Streams

A Multilinear Subspace Regression Method Using Orthogonal Tensors Decompositions