NIPS 2010 Papers

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Non-Stochastic Bandit Slate Problems

Tiled convolutional neural networks

Accounting for network effects in neuronal responses using L1 regularized point process models

Gaussian sampling by local perturbations

A Dirty Model for Multi-task Learning

Short-term memory in neuronal networks through dynamical compressed sensing

MAP Estimation for Graphical Models by Likelihood Maximization

Switched Latent Force Models for Movement Segmentation

Learning concept graphs from text with stick-breaking priors

Linear readout from a neural population with partial correlation data

Learning the context of a category

Learning Convolutional Feature Hierarchies for Visual Recognition

An Alternative to Low-level-Sychrony-Based Methods for Speech Detection

Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains

Variational Inference over Combinatorial Spaces

Sparse Inverse Covariance Selection via Alternating Linearization Methods

Multiple Kernel Learning and the SMO Algorithm

Robust PCA via Outlier Pursuit

Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine

Active Learning Applied to Patient-Adaptive Heartbeat Classification

Universal Kernels on Non-Standard Input Spaces

Spike timing-dependent plasticity as dynamic filter

A Log-Domain Implementation of the Diffusion Network in Very Large Scale Integration

Rates of convergence for the cluster tree

Layered image motion with explicit occlusions, temporal consistency, and depth ordering

Learning To Count Objects in Images

Kernel Descriptors for Visual Recognition

Lower Bounds on Rate of Convergence of Cutting Plane Methods

Evaluating neuronal codes for inference using Fisher information

Learning from Candidate Labeling Sets

On the Theory of Learnining with Privileged Information

Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference

Smoothness, Low Noise and Fast Rates

A Bayesian Approach to Concept Drift

A unified model of short-range and long-range motion perception

Sparse Instrumental Variables (SPIV) for Genome-Wide Studies

Auto-Regressive HMM Inference with Incomplete Data for Short-Horizon Wind Forecasting

Sidestepping Intractable Inference with Structured Ensemble Cascades

Convex Multiple-Instance Learning by Estimating Likelihood Ratio

Regularized estimation of image statistics by Score Matching

Random Conic Pursuit for Semidefinite Programming

Towards Property-Based Classification of Clustering Paradigms

Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning

Trading off Mistakes and Don't-Know Predictions

Online Markov Decision Processes under Bandit Feedback

Humans Learn Using Manifolds, Reluctantly

Online Learning for Latent Dirichlet Allocation

Probabilistic Inference and Differential Privacy

Structural epitome: a way to summarize one’s visual experience

Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression

Energy Disaggregation via Discriminative Sparse Coding

Batch Bayesian Optimization via Simulation Matching

Probabilistic Deterministic Infinite Automata

Online Learning: Random Averages, Combinatorial Parameters, and Learnability

A Theory of Multiclass Boosting

On the Convexity of Latent Social Network Inference

Sphere Embedding: An Application to Part-of-Speech Induction

Bootstrapping Apprenticeship Learning

Size Matters: Metric Visual Search Constraints from Monocular Metadata

Linear Complementarity for Regularized Policy Evaluation and Improvement

Probabilistic latent variable models for distinguishing between cause and effect

Predictive State Temporal Difference Learning

An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA

Repeated Games against Budgeted Adversaries

Evidence-Specific Structures for Rich Tractable CRFs

Nonparametric Bayesian Policy Priors for Reinforcement Learning

Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition

SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system

b-Bit Minwise Hashing for Estimating Three-Way Similarities

The LASSO risk: asymptotic results and real world examples

CUR from a Sparse Optimization Viewpoint

Infinite Relational Modeling of Functional Connectivity in Resting State fMRI

Learning Multiple Tasks using Manifold Regularization

Fast detection of multiple change-points shared by many signals using group LARS

Empirical Bernstein Inequalities for U-Statistics

Parallelized Stochastic Gradient Descent

Sample Complexity of Testing the Manifold Hypothesis

Generating more realistic images using gated MRF's

Near-Optimal Bayesian Active Learning with Noisy Observations

Exact learning curves for Gaussian process regression on large random graphs

Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization

Global Analytic Solution for Variational Bayesian Matrix Factorization

Efficient Optimization for Discriminative Latent Class Models

Learning Efficient Markov Networks

Approximate inference in continuous time Gaussian-Jump processes

Deep Coding Network

Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication

Co-regularization Based Semi-supervised Domain Adaptation

Brain covariance selection: better individual functional connectivity models using population prior

Error Propagation for Approximate Policy and Value Iteration

Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories

Fast global convergence rates of gradient methods for high-dimensional statistical recovery

Network Flow Algorithms for Structured Sparsity

An analysis on negative curvature induced by singularity in multi-layer neural-network learning

A Bayesian Framework for Figure-Ground Interpretation

Inference and communication in the game of Password

New Adaptive Algorithms for Online Classification

A rational decision making framework for inhibitory control

Online Learning in The Manifold of Low-Rank Matrices

Evaluation of Rarity of Fingerprints in Forensics

Minimum Average Cost Clustering

Improvements to the Sequence Memoizer

Throttling Poisson Processes

Getting lost in space: Large sample analysis of the resistance distance

Relaxed Clipping: A Global Training Method for Robust Regression and Classification

Identifying graph-structured activation patterns in networks

Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics

Basis Construction from Power Series Expansions of Value Functions

Joint Cascade Optimization Using A Product Of Boosted Classifiers

Penalized Principal Component Regression on Graphs for Analysis of Subnetworks

Bayesian Action-Graph Games

Multi-View Active Learning in the Non-Realizable Case

Structured Determinantal Point Processes

Shadow Dirichlet for Restricted Probability Modeling

Random Walk Approach to Regret Minimization

Direct Loss Minimization for Structured Prediction

Subgraph Detection Using Eigenvector L1 Norms

Link Discovery using Graph Feature Tracking

Improving Human Judgments by Decontaminating Sequential Dependencies

Reward Design via Online Gradient Ascent

Deterministic Single-Pass Algorithm for LDA

A Computational Decision Theory for Interactive Assistants

Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model

Pose-Sensitive Embedding by Nonlinear NCA Regression

Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development

Implicit encoding of prior probabilities in optimal neural populations

Structured sparsity-inducing norms through submodular functions

Parametric Bandits: The Generalized Linear Case

Variational bounds for mixed-data factor analysis

Learning to combine foveal glimpses with a third-order Boltzmann machine

Empirical Risk Minimization with Approximations of Probabilistic Grammars

A biologically plausible network for the computation of orientation dominance

Movement extraction by detecting dynamics switches and repetitions

Sodium entry efficiency during action potentials: A novel single-parameter family of Hodgkin-Huxley models

Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks

Slice sampling covariance hyperparameters of latent Gaussian models

On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient

Probabilistic Belief Revision with Structural Constraints

Fractionally Predictive Spiking Neurons

Spectral Regularization for Support Estimation

Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm

Copula Processes

Scrambled Objects for Least-Squares Regression

Unsupervised Kernel Dimension Reduction

Global seismic monitoring as probabilistic inference

Functional form of motion priors in human motion perception

Multitask Learning without Label Correspondences

Learning from Logged Implicit Exploration Data

Approximate Inference by Compilation to Arithmetic Circuits

Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models

A POMDP Extension with Belief-dependent Rewards

Self-Paced Learning for Latent Variable Models

A Primal-Dual Algorithm for Group Sparse Regularization with Overlapping Groups

Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions

More data means less inference: A pseudo-max approach to structured learning

Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models

LSTD with Random Projections

Phoneme Recognition with Large Hierarchical Reservoirs

Causal discovery in multiple models from different experiments

The Maximal Causes of Natural Scenes are Edge Filters

Heavy-Tailed Process Priors for Selective Shrinkage

Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning

Efficient Relational Learning with Hidden Variable Detection

Avoiding False Positive in Multi-Instance Learning

Feature Set Embedding for Incomplete Data

Inference with Multivariate Heavy-Tails in Linear Models

Learning Networks of Stochastic Differential Equations

A Reduction from Apprenticeship Learning to Classification

The Neural Costs of Optimal Control

A novel family of non-parametric cumulative based divergences for point processes

Static Analysis of Binary Executables Using Structural SVMs

Reverse Multi-Label Learning

An Approximate Inference Approach to Temporal Optimization in Optimal Control

Semi-Supervised Learning with Adversarially Missing Label Information

Learning Bounds for Importance Weighting

Monte-Carlo Planning in Large POMDPs

Extensions of Generalized Binary Search to Group Identification and Exponential Costs

Word Features for Latent Dirichlet Allocation

Practical Large-Scale Optimization for Max-norm Regularization

Multi-Stage Dantzig Selector

Boosting Classifier Cascades

Guaranteed Rank Minimization via Singular Value Projection

Active Estimation of F-Measures

Moreau-Yosida Regularization for Grouped Tree Structure Learning

Estimating Spatial Layout of Rooms using Volumetric Reasoning about Objects and Surfaces

Large Margin Multi-Task Metric Learning

Optimal learning rates for Kernel Conjugate Gradient regression

Policy gradients in linearly-solvable MDPs

Optimal Web-Scale Tiering as a Flow Problem

Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations

Active Learning by Querying Informative and Representative Examples

Identifying Dendritic Processing

Worst-Case Linear Discriminant Analysis

Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake

Active Instance Sampling via Matrix Partition

Decoding Ipsilateral Finger Movements from ECoG Signals in Humans

A Family of Penalty Functions for Structured Sparsity

The Multidimensional Wisdom of Crowds

Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors

Gaussian Process Preference Elicitation

Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles

Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks

Effects of Synaptic Weight Diffusion on Learning in Decision Making Networks

Adaptive Multi-Task Lasso: with Application to eQTL Detection

PAC-Bayesian Model Selection for Reinforcement Learning

Discriminative Clustering by Regularized Information Maximization

Inductive Regularized Learning of Kernel Functions

On Herding and the Perceptron Cycling Theorem

Distributed Dual Averaging In Networks

Synergies in learning words and their referents

Attractor Dynamics with Synaptic Depression

A VLSI Implementation of the Adaptive Exponential Integrate-and-Fire Neuron Model

Implicit Differentiation by Perturbation

Learning Multiple Tasks with a Sparse Matrix-Normal Penalty

Lifted Inference Seen from the Other Side : The Tractable Features

Group Sparse Coding with a Laplacian Scale Mixture Prior

Mixture of time-warped trajectory models for movement decoding

Factorized Latent Spaces with Structured Sparsity

Learning invariant features using the Transformed Indian Buffet Process

Efficient Minimization of Decomposable Submodular Functions

A Novel Kernel for Learning a Neuron Model from Spike Train Data

Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets

Predictive Subspace Learning for Multi-view Data: a Large Margin Approach

Graph-Valued Regression

Worst-case bounds on the quality of max-product fixed-points

Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable

Supervised Clustering

Variable margin losses for classifier design

Why are some word orders more common than others? A uniform information density account

Joint Analysis of Time-Evolving Binary Matrices and Associated Documents

Using body-anchored priors for identifying actions in single images

MAP estimation in Binary MRFs via Bipartite Multi-cuts

Sparse Coding for Learning Interpretable Spatio-Temporal Primitives

A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction

Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework

Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers

Learning Kernels with Radiuses of Minimum Enclosing Balls

Generative Local Metric Learning for Nearest Neighbor Classification

A New Probabilistic Model for Rank Aggregation

Agnostic Active Learning Without Constraints

Simultaneous Object Detection and Ranking with Weak Supervision

Cross Species Expression Analysis using a Dirichlet Process Mixture Model with Latent Matchings

Robust Clustering as Ensembles of Affinity Relations

Dynamic Infinite Relational Model for Time-varying Relational Data Analysis

Learning via Gaussian Herding

Large Margin Learning of Upstream Scene Understanding Models

Probabilistic Multi-Task Feature Selection

(RF)^2 -- Random Forest Random Field

Gated Softmax Classification

Distributionally Robust Markov Decision Processes

Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices

Multivariate Dyadic Regression Trees for Sparse Learning Problems

Tight Sample Complexity of Large-Margin Learning

Feature Transitions with Saccadic Search: Size, Color, and Orientation Are Not Alike

Label Embedding Trees for Large Multi-Class Tasks

Exact inference and learning for cumulative distribution functions on loopy graphs

Online Classification with Specificity Constraints

Decomposing Isotonic Regression for Efficiently Solving Large Problems

Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs

Double Q-learning

Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation

Over-complete representations on recurrent neural networks can support persistent percepts

Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch

Spatial and anatomical regularization of SVM for brain image analysis

Layer-wise analysis of deep networks with Gaussian kernels

Switching state space model for simultaneously estimating state transitions and nonstationary firing rates

Fast Large-scale Mixture Modeling with Component-specific Data Partitions

Copula Bayesian Networks

Feature Construction for Inverse Reinforcement Learning

Epitome driven 3-D Diffusion Tensor image segmentation: on extracting specific structures

t-logistic regression

Learning to localise sounds with spiking neural networks

A Discriminative Latent Model of Image Region and Object Tag Correspondence

Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification

Divisive Normalization: Justification and Effectiveness as Efficient Coding Transform

Tree-Structured Stick Breaking for Hierarchical Data

Beyond Actions: Discriminative Models for Contextual Group Activities

Random Projections for $k$-means Clustering

Random Projection Trees Revisited

Segmentation as Maximum-Weight Independent Set

Transduction with Matrix Completion: Three Birds with One Stone

Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models

Functional Geometry Alignment and Localization of Brain Areas

Accounting for network effects in neuronal responses using L1 regularized point process models

Construction of Dependent Dirichlet Processes based on Poisson Processes

Universal Consistency of Multi-Class Support Vector Classiﬁcation

Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach

Occlusion Detection and Motion Estimation with Convex Optimization

Large-Scale Matrix Factorization with Missing Data under Additional Constraints

Two-Layer Generalization Analysis for Ranking Using Rademacher Average

Extended Bayesian Information Criteria for Gaussian Graphical Models

Permutation Complexity Bound on Out-Sample Error

Generalized roof duality and bisubmodular functions