NeurIPS(NIPS) 2010 论文列表Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada. |
Parallelized Stochastic Gradient Descent.
Large Margin Learning of Upstream Scene Understanding Models.
Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework.
Worst-Case Linear Discriminant Analysis.
Probabilistic Multi-Task Feature Selection.
Lower Bounds on Rate of Convergence of Cutting Plane Methods.
Learning Multiple Tasks with a Sparse Matrix-Normal Penalty.
Feature Transitions with Saccadic Search: Size, Color, and Orientation Are Not Alike.
Relaxed Clipping: A Global Training Method for Robust Regression and Classification.
Robust PCA via Outlier Pursuit.
Inference and communication in the game of Password.
Distributionally Robust Markov Decision Processes.
A unified model of short-range and long-range motion perception.
A Log-Domain Implementation of the Diffusion Network in Very Large Scale Integration.
Linear readout from a neural population with partial correlation data.
Copula Processes.
Probabilistic Inference and Differential Privacy.
Active Learning Applied to Patient-Adaptive Heartbeat Classification.
Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains.
The Multidimensional Wisdom of Crowds.
Sidestepping Intractable Inference with Structured Ensemble Cascades.
Heavy-Tailed Process Priors for Selective Shrinkage.
Joint Analysis of Time-Evolving Binary Matrices and Associated Documents.
A Discriminative Latent Model of Image Region and Object Tag Correspondence.
Multi-View Active Learning in the Non-Realizable Case.
Unsupervised Kernel Dimension Reduction.
Spectral Regularization for Support Estimation.
Multiple Kernel Learning and the SMO Algorithm.
Worst-case bounds on the quality of max-product fixed-points.
Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets.
Fast detection of multiple change-points shared by many signals using group LARS.
Brain covariance selection: better individual functional connectivity models using population prior.
Exact learning curves for Gaussian process regression on large random graphs.
Phoneme Recognition with Large Hierarchical Reservoirs.
Policy gradients in linearly-solvable MDPs.
Fast Large-scale Mixture Modeling with Component-specific Data Partitions.
Pose-Sensitive Embedding by Nonlinear NCA Regression.
Switching state space model for simultaneously estimating state transitions and nonstationary firing rates.
Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch.
Semi-Supervised Learning with Adversarially Missing Label Information.
A Reduction from Apprenticeship Learning to Classification.
Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices.
Layered image motion with explicit occlusions, temporal consistency, and depth ordering.
Learning from Logged Implicit Exploration Data.
Efficient Minimization of Decomposable Submodular Functions.
Smoothness, Low Noise and Fast Rates.
Reward Design via Online Gradient Ascent.
More data means less inference: A pseudo-max approach to structured learning.
Sodium entry efficiency during action potentials: A novel single-parameter family of Hodgkin-Huxley models.
Monte-Carlo Planning in Large POMDPs.
Penalized Principal Component Regression on Graphs for Analysis of Subnetworks.
A rational decision making framework for inhibitory control.
Identifying graph-structured activation patterns in networks.
Online Learning in The Manifold of Low-Rank Matrices.
A novel family of non-parametric cumulative based divergences for point processes.
Sohan Seth Il Memming Park Austin J. Brockmeier Mulugeta Semework John S. Choi Joseph T. Francis José Carlos Príncipe
Spike timing-dependent plasticity as dynamic filter.
Sparse Inverse Covariance Selection via Alternating Linearization Methods.
Trading off Mistakes and Don't-Know Predictions.
Active Estimation of F-Measures.
Deterministic Single-Pass Algorithm for LDA.
Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation.
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm.
Boosting Classifier Cascades.
Tight Sample Complexity of Large-Margin Learning.
An Alternative to Low-level-Sychrony-Based Methods for Speech Detection.
Link Discovery using Graph Feature Tracking.
Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model.
MAP estimation in Binary MRFs via Bipartite Multi-cuts.
An Approximate Inference Approach to Temporal Optimization in Optimal Control.
Generating more realistic images using gated MRF's.
Evaluating neuronal codes for inference using Fisher information.
Online Learning: Random Averages, Combinatorial Parameters, and Learnability.
Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics.
Multitask Learning without Label Correspondences.
A New Probabilistic Model for Rank Aggregation.
The Maximal Causes of Natural Scenes are Edge Filters.
Probabilistic Deterministic Infinite Automata.
Reverse Multi-Label Learning.
Word Features for Latent Dirichlet Allocation.
Learning Networks of Stochastic Differential Equations.
Empirical Bernstein Inequalities for U-Statistics.
On the Theory of Learnining with Privileged Information.
(RF)^2 - Random Forest Random Field.
Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers.
Large Margin Multi-Task Metric Learning.
Gaussian sampling by local perturbations.
Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs.
New Adaptive Algorithms for Online Classification.
Approximate inference in continuous time Gaussian-Jump processes.
Generative Local Metric Learning for Nearest Neighbor Classification.
Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization.
Online Markov Decision Processes under Bandit Feedback.
Learning the context of a category.
Sample Complexity of Testing the Manifold Hypothesis.
Random Walk Approach to Regret Minimization.
Global Analytic Solution for Variational Bayesian Matrix Factorization.
Minimum Average Cost Clustering.
Infinite Relational Modeling of Functional Connectivity in Resting State fMRI.
On the Convexity of Latent Social Network Inference.
Slice sampling covariance hyperparameters of latent Gaussian models.
A biologically plausible network for the computation of orientation dominance.
A Theory of Multiclass Boosting.
Improving Human Judgments by Decontaminating Sequential Dependencies.
Epitome driven 3-D Diffusion Tensor image segmentation: on extracting specific structures.
A Primal-Dual Algorithm for Group Sparse Regularization with Overlapping Groups.
Probabilistic latent variable models for distinguishing between cause and effect.
Layer-wise analysis of deep networks with Gaussian kernels.
An analysis on negative curvature induced by singularity in multi-layer neural-network learning.
Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks.
Large-Scale Matrix Factorization with Missing Data under Additional Constraints.
A VLSI Implementation of the Adaptive Exponential Integrate-and-Fire Neuron Model.
Subgraph Detection Using Eigenvector L1 Norms.
A Family of Penalty Functions for Structured Sparsity.
Gated Softmax Classification.
Direct Loss Minimization for Structured Prediction.
Why are some word orders more common than others? A uniform information density account.
Variable margin losses for classifier design.
Sphere Embedding: An Application to Part-of-Speech Induction.
Network Flow Algorithms for Structured Sparsity.
Scrambled Objects for Least-Squares Regression.
Basis Construction from Power Series Expansions of Value Functions.
Permutation Complexity Bound on Out-Sample Error.
Divisive Normalization: Justification and Effectiveness as Efficient Coding Transform.
Getting lost in space: Large sample analysis of the resistance distance.
Decomposing Isotonic Regression for Efficiently Solving Large Problems.
Learning from Candidate Labeling Sets.
Functional form of motion priors in human motion perception.
Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference.
Approximate Inference by Compilation to Arithmetic Circuits.
Decoding Ipsilateral Finger Movements from ECoG Signals in Humans.
Yuzong Liu Mohit Sharma Charles M. Gaona Jonathan Breshears Jarod Roland Zachary Freudenburg Kilian Q. Weinberger Eric Leuthardt
Multi-Stage Dantzig Selector.
Graph-Valued Regression.
Robust Clustering as Ensembles of Affinity Relations.
Multivariate Dyadic Regression Trees for Sparse Learning Problems.
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models.
Moreau-Yosida Regularization for Grouped Tree Structure Learning.
Deep Coding Network.
Construction of Dependent Dirichlet Processes based on Poisson Processes.
b-Bit Minwise Hashing for Estimating Three-Way Similarities.
Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models.
Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles.
Kaiming Li Lei Guo Carlos Faraco Dajiang Zhu Fan Deng Tuo Zhang Xi Jiang Degang Zhang Hanbo Chen Xintao Hu L. Stephen Miller Tianming Liu
Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification.
Convex Multiple-Instance Learning by Estimating Likelihood Ratio.
Feature Construction for Inverse Reinforcement Learning.
Optimal Web-Scale Tiering as a Flow Problem.
Learning To Count Objects in Images.
Joint Cascade Optimization Using A Product Of Boosted Classifiers.
Practical Large-Scale Optimization for Max-norm Regularization.
Adaptive Multi-Task Lasso: with Application to eQTL Detection.
Estimating Spatial Layout of Rooms using Volumetric Reasoning about Objects and Surfaces.
Evaluation of Rarity of Fingerprints in Forensics.
Cross Species Expression Analysis using a Dirichlet Process Mixture Model with Latent Matchings.
Tiled convolutional neural networks.
Identifying Dendritic Processing.
Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations.
Learning to combine foveal glimpses with a third-order Boltzmann machine.
Efficient Relational Learning with Hidden Variable Detection.
Functional Geometry Alignment and Localization of Brain Areas.
Beyond Actions: Discriminative Models for Contextual Group Activities.
Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions.
Self-Paced Learning for Latent Variable Models.
MAP Estimation for Graphical Models by Likelihood Maximization.
Structured Determinantal Point Processes.
Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories.
Energy Disaggregation via Discriminative Sparse Coding.
Generalized roof duality and bisubmodular functions.
Random Conic Pursuit for Semidefinite Programming.
Regularized estimation of image statistics by Score Matching.
Sparse Coding for Learning Interpretable Spatio-Temporal Primitives.
Variational bounds for mixed-data factor analysis.
Accounting for network effects in neuronal responses using L1 regularized point process models.
Learning Convolutional Feature Hierarchies for Visual Recognition.
Effects of Synaptic Weight Diffusion on Learning in Decision Making Networks.
Using body-anchored priors for identifying actions in single images.
Static Analysis of Binary Executables Using Structural SVMs.
Non-Stochastic Bandit Slate Problems.
Efficient Optimization for Discriminative Latent Class Models.
Probabilistic Belief Revision with Structural Constraints.
Structural epitome: a way to summarize one's visual experience.
Synergies in learning words and their referents.
Linear Complementarity for Regularized Policy Evaluation and Improvement.
On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient.
Bayesian Action-Graph Games.
Factorized Latent Spaces with Structured Sparsity.
Lifted Inference Seen from the Other Side : The Tractable Features.
A Dirty Model for Multi-task Learning.
Guaranteed Rank Minimization via Singular Value Projection.
Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning.
Inductive Regularized Learning of Kernel Functions.
Dynamic Infinite Relational Model for Time-varying Relational Data Analysis.
Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication.
Co-regularization Based Semi-supervised Domain Adaptation.
Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks.
Exact inference and learning for cumulative distribution functions on loopy graphs.
Active Learning by Querying Informative and Representative Examples.
Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression.
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development.
Online Learning for Latent Dirichlet Allocation.
An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA.
A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction.
Double Q-learning.
Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake.
Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable.
Avoiding False Positive in Multi-Instance Learning.
Active Instance Sampling via Matrix Partition.
Feature Set Embedding for Incomplete Data.
Learning to localise sounds with spiking neural networks.
Discriminative Clustering by Regularized Information Maximization.
Near-Optimal Bayesian Active Learning with Noisy Observations.
Transduction with Matrix Completion: Three Birds with One Stone.
Learning Efficient Markov Networks.
Universal Consistency of Multi-Class Support Vector Classification.
Humans Learn Using Manifolds, Reluctantly.
LSTD with Random Projections.
The Neural Costs of Optimal Control.
Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models.
On Herding and the Perceptron Cycling Theorem.
Improvements to the Sequence Memoizer.
Group Sparse Coding with a Laplacian Scale Mixture Prior.
Implicit encoding of prior probabilities in optimal neural populations.
Short-term memory in neuronal networks through dynamical compressed sensing.
Learning Kernels with Radiuses of Minimum Enclosing Balls.
Attractor Dynamics with Synaptic Depression.
A Bayesian Framework for Figure-Ground Interpretation.
Size Matters: Metric Visual Search Constraints from Monocular Metadata.
Shadow Dirichlet for Restricted Probability Modeling.
Extended Bayesian Information Criteria for Gaussian Graphical Models.
A Novel Kernel for Learning a Neuron Model from Spike Train Data.
Parametric Bandits: The Generalized Linear Case.
A Computational Decision Theory for Interactive Assistants.
PAC-Bayesian Model Selection for Reinforcement Learning.
Error Propagation for Approximate Policy and Value Iteration.
Copula Bayesian Networks.
Distributed Dual Averaging In Networks.
Over-complete representations on recurrent neural networks can support persistent percepts.
Nonparametric Bayesian Policy Priors for Reinforcement Learning.
Implicit Differentiation by Perturbation.
t-logistic regression.
Throttling Poisson Processes.
Random Projection Trees Revisited.
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine.
Spatial and anatomical regularization of SVM for brain image analysis.
Learning via Gaussian Herding.
Learning Bounds for Importance Weighting.
Mixture of time-warped trajectory models for movement decoding.
Empirical Risk Minimization with Approximations of Probabilistic Grammars.
Causal discovery in multiple models from different experiments.
Universal Kernels on Non-Standard Input Spaces.
Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors.
Movement extraction by detecting dynamics switches and repetitions.
SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system.
Two-Layer Generalization Analysis for Ranking Using Rademacher Average.
Predictive Subspace Learning for Multi-view Data: a Large Margin Approach.
Evidence-Specific Structures for Rich Tractable CRFs.
Rates of convergence for the cluster tree.
Learning concept graphs from text with stick-breaking priors.
Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition.
Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning.
Segmentation as Maximum-Weight Independent Set.
Random Projections for $k$-means Clustering.
Bootstrapping Apprenticeship Learning.
Variational Inference over Combinatorial Spaces.
Predictive State Temporal Difference Learning.
Gaussian Process Preference Elicitation.
Fractionally Predictive Spiking Neurons.
Kernel Descriptors for Visual Recognition.
Simultaneous Object Detection and Ranking with Weak Supervision.
Optimal learning rates for Kernel Conjugate Gradient regression.
CUR from a Sparse Optimization Viewpoint.
Inference with Multivariate Heavy-Tails in Linear Models.
Agnostic Active Learning Without Constraints.
Online Classification with Specificity Constraints.
Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach.
Label Embedding Trees for Large Multi-Class Tasks.
Extensions of Generalized Binary Search to Group Identification and Exponential Costs.
The LASSO risk: asymptotic results and real world examples.
Auto-Regressive HMM Inference with Incomplete Data for Short-Horizon Wind Forecasting.
A Bayesian Approach to Concept Drift.
Structured sparsity-inducing norms through submodular functions.
Batch Bayesian Optimization via Simulation Matching.
Occlusion Detection and Motion Estimation with Convex Optimization.
Supervised Clustering.
Learning invariant features using the Transformed Indian Buffet Process.
Global seismic monitoring as probabilistic inference.
A POMDP Extension with Belief-dependent Rewards.
Switched Latent Force Models for Movement Segmentation.
Learning Multiple Tasks using Manifold Regularization.
Fast global convergence rates of gradient methods for high-dimensional statistical recovery.
Sparse Instrumental Variables (SPIV) for Genome-Wide Studies.
Tree-Structured Stick Breaking for Hierarchical Data.
Towards Property-Based Classification of Clustering Paradigms.
Repeated Games against Budgeted Adversaries.