NeurIPS(NIPS) 2013 论文列表Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States. |
Robust Low Rank Kernel Embeddings of Multivariate Distributions.
Learning to Pass Expectation Propagation Messages.
Matrix factorization with binary components.
Optimal integration of visual speed across different spatiotemporal frequency channels.
Adaptive Anonymity via b-Matching.
Multisensory Encoding, Decoding, and Identification.
The Fast Convergence of Incremental PCA.
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables.
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC.
Scalable Influence Estimation in Continuous-Time Diffusion Networks.
Recurrent linear models of simultaneously-recorded neural populations.
Analyzing the Harmonic Structure in Graph-Based Learning.
Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel.
Distributed Representations of Words and Phrases and their Compositionality.
Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex.
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream.
Adaptive dropout for training deep neural networks.
Adaptivity to Local Smoothness and Dimension in Kernel Regression.
Optimization, Learning, and Games with Predictable Sequences.
Learning invariant representations and applications to face verification.
Learning Stochastic Inverses.
Learning and using language via recursive pragmatic reasoning about other agents.
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces.
Efficient Exploration and Value Function Generalization in Deterministic Systems.
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition.
(More) Efficient Reinforcement Learning via Posterior Sampling.
Sketching Structured Matrices for Faster Nonlinear Regression.
Convex Two-Layer Modeling.
Variational Planning for Graph-based MDPs.
A New Convex Relaxation for Tensor Completion.
Context-sensitive active sensing in humans.
Fast Template Evaluation with Vector Quantization.
Moment-based Uniform Deviation Bounds for k-means and Friends.
Compressive Feature Learning.
Bayesian inference as iterated random functions with applications to sequential inference in graphical models.
On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation.
Linear decision rule as aspiration for simple decision heuristics.
An Approximate, Efficient LP Solver for LP Rounding.
Memory Limited, Streaming PCA.
On the Expressive Power of Restricted Boltzmann Machines.
On the Complexity and Approximation of Binary Evidence in Lifted Inference.
Learning from Limited Demonstrations.
Modeling Overlapping Communities with Node Popularities.
A multi-agent control framework for co-adaptation in brain-computer interfaces.
Estimation, Optimization, and Parallelism when Data is Sparse.
The Power of Asymmetry in Binary Hashing.
Understanding Dropout.
Real-Time Inference for a Gamma Process Model of Neural Spiking.
Phase Retrieval using Alternating Minimization.
Translating Embeddings for Modeling Multi-relational Data.
Optimizing Instructional Policies.
Annealing between distributions by averaging moments.
Learning Kernels Using Local Rademacher Complexity.
Σ-Optimality for Active Learning on Gaussian Random Fields.
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions.
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings.
Minimax Optimal Algorithms for Unconstrained Linear Optimization.
Analyzing Hogwild Parallel Gaussian Gibbs Sampling.
Generalized Method-of-Moments for Rank Aggregation.
Adaptive Submodular Maximization in Bandit Setting.
Bayesian inference for low rank spatiotemporal neural receptive fields.
Cluster Trees on Manifolds.
Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA.
Capacity of strong attractor patterns to model behavioural and cognitive prototypes.
A Stability-based Validation Procedure for Differentially Private Machine Learning.
Deep content-based music recommendation.
Multilinear Dynamical Systems for Tensor Time Series.
Policy Shaping: Integrating Human Feedback with Reinforcement Learning.
Probabilistic Movement Primitives.
Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting.
Restricting exchangeable nonparametric distributions.
k-Prototype Learning for 3D Rigid Structures.
Relevance Topic Model for Unstructured Social Group Activity Recognition.
Sign Cauchy Projections and Chi-Square Kernel.
Geometric optimisation on positive definite matrices for elliptically contoured distributions.
Deep Neural Networks for Object Detection.
Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis.
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators.
One-shot learning by inverting a compositional causal process.
Flexible sampling of discrete data correlations without the marginal distributions.
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions.
Symbolic Opportunistic Policy Iteration for Factored-Action MDPs.
Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions.
Integrated Non-Factorized Variational Inference.
Synthesizing Robust Plans under Incomplete Domain Models.
Universal models for binary spike patterns using centered Dirichlet processes.
Spectral methods for neural characterization using generalized quadratic models.
Scalable Inference for Logistic-Normal Topic Models.
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints.
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited.
A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables.
Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization.
Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising.
Robust learning of low-dimensional dynamics from large neural ensembles.
Aggregating Optimistic Planning Trees for Solving Markov Decision Processes.
Learning the Local Statistics of Optical Flow.
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion.
Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests.
How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal.
Projected Natural Actor-Critic.
Information-theoretic lower bounds for distributed statistical estimation with communication constraints.
Fast Determinantal Point Process Sampling with Application to Clustering.
Compete to Compute.
Speedup Matrix Completion with Side Information: Application to Multi-Label Learning.
Sinkhorn Distances: Lightspeed Computation of Optimal Transport.
Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization.
Sparse Inverse Covariance Estimation with Calibration.
Learning word embeddings efficiently with noise-contrastive estimation.
Eluder Dimension and the Sample Complexity of Optimistic Exploration.
Message Passing Inference with Chemical Reaction Networks.
Contrastive Learning Using Spectral Methods.
Buy-in-Bulk Active Learning.
Sequential Transfer in Multi-armed Bandit with Finite Set of Models.
Sensor Selection in High-Dimensional Gaussian Trees with Nuisances.
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis.
Reconciling "priors" & "priors" without prejudice?
Two-Target Algorithms for Infinite-Armed Bandits with Bernoulli Rewards.
RNADE: The real-valued neural autoregressive density-estimator.
What do row and column marginals reveal about your dataset?
Estimating the Unseen: Improved Estimators for Entropy and other Properties.
Predicting Parameters in Deep Learning.
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation.
Reward Mapping for Transfer in Long-Lived Agents.
DeViSE: A Deep Visual-Semantic Embedding Model.
Andrea Frome Gregory S. Corrado Jonathon Shlens Samy Bengio Jeffrey Dean Marc'Aurelio Ranzato Tomás Mikolov
Convergence of Monte Carlo Tree Search in Simultaneous Move Games.
Small-Variance Asymptotics for Hidden Markov Models.
Discriminative Transfer Learning with Tree-based Priors.
Embed and Project: Discrete Sampling with Universal Hashing.
Spike train entropy-rate estimation using hierarchical Dirichlet process priors.
On the Sample Complexity of Subspace Learning.
Adaptive Market Making via Online Learning.
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data.
Approximate Gaussian process inference for the drift function in stochastic differential equations.
Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space.
A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-Sized Cycles.
Online Learning of Dynamic Parameters in Social Networks.
Multi-Task Bayesian Optimization.
Distributed k-means and k-median clustering on general communication topologies.
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity.
Learning Multiple Models via Regularized Weighting.
Demixing odors - fast inference in olfaction.
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic.
Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation.
Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model.
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data.
Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths.
Scoring Workers in Crowdsourcing: How Many Control Questions are Enough?
Machine Teaching for Bayesian Learners in the Exponential Family.
Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms.
Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions.
Top-Down Regularization of Deep Belief Networks.
Generalizing Analytic Shrinkage for Arbitrary Covariance Structures.
Solving the multi-way matching problem by permutation synchronization.
Robust Bloom Filters for Large MultiLabel Classification Tasks.
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies.
Learning Gaussian Graphical Models with Observed or Latent FVSs.
On Flat versus Hierarchical Classification in Large-Scale Taxonomies.
Stochastic Optimization of PCA with Capped MSG.
Dimension-Free Exponentiated Gradient.
Multiscale Dictionary Learning for Estimating Conditional Distributions.
Regression-tree Tuning in a Streaming Setting.
Matrix Completion From any Given Set of Observations.
DESPOT: Online POMDP Planning with Regularization.
Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections.
Approximate Dynamic Programming Finally Performs Well in the Game of Tetris.
Extracting regions of interest from biological images with convolutional sparse block coding.
Gaussian Process Conditional Copulas with Applications to Financial Time Series.
Streaming Variational Bayes.
On Poisson Graphical Models.
Perfect Associative Learning with Spike-Timing-Dependent Plasticity.
Bayesian entropy estimation for binary spike train data using parametric prior knowledge.
Exact and Stable Recovery of Pairwise Interaction Tensors.
Noise-Enhanced Associative Memories.
Mapping paradigm ontologies to and from the brain.
Locally Adaptive Bayesian Multivariate Time Series.
Solving inverse problem of Markov chain with partial observations.
Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search.
Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors.
Pass-efficient unsupervised feature selection.
Predictive PAC Learning and Process Decompositions.
From Bandits to Experts: A Tale of Domination and Independence.
Bayesian Hierarchical Community Discovery.
Optimistic policy iteration and natural actor-critic: A unifying view and a non-optimality result.
Online learning in episodic Markovian decision processes by relative entropy policy search.
Learning to Prune in Metric and Non-Metric Spaces.
Near-Optimal Entrywise Sampling for Data Matrices.
Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty.
Manifold-based Similarity Adaptation for Label Propagation.
Firing rate predictions in optimal balanced networks.
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation.
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs.
Similarity Component Analysis.
EDML for Learning Parameters in Directed and Undirected Graphical Models.
Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising.
Inverse Density as an Inverse Problem: the Fredholm Equation Approach.
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses.
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals.
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion.
Thompson Sampling for 1-Dimensional Exponential Family Bandits.
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering.
Approximate Inference in Continuous Determinantal Processes.
Multiclass Total Variation Clustering.
Reservoir Boosting : Between Online and Offline Ensemble Learning.
Optimistic Concurrency Control for Distributed Unsupervised Learning.
Adaptive Step-Size for Policy Gradient Methods.
Nonparametric Multi-group Membership Model for Dynamic Networks.
Computing the Stationary Distribution Locally.
A Deep Architecture for Matching Short Texts.
Parametric Task Learning.
Learning Chordal Markov Networks by Constraint Satisfaction.
Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs.
Convex Tensor Decomposition via Structured Schatten Norm Regularization.
Unsupervised Structure Learning of Stochastic And-Or Grammars.
Reflection methods for user-friendly submodular optimization.
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits.
Statistical Active Learning Algorithms.
Reshaping Visual Datasets for Domain Adaptation.
Graphical Models for Inference with Missing Data.
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations.
A Novel Two-Step Method for Cross Language Representation Learning.
Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions.
Online Learning with Costly Features and Labels.
Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models.
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server.
Qirong Ho James Cipar Henggang Cui Seunghak Lee Jin Kyu Kim Phillip B. Gibbons Garth A. Gibson Gregory R. Ganger Eric P. Xing
Factorized Asymptotic Bayesian Inference for Latent Feature Models.
Tracking Time-varying Graphical Structure.
Learning with Noisy Labels.
Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models.
Probabilistic Principal Geodesic Analysis.
Learning Prices for Repeated Auctions with Strategic Buyers.
Online Learning with Switching Costs and Other Adaptive Adversaries.
Robust Transfer Principal Component Analysis with Rank Constraints.
Designed Measurements for Vector Count Data.
Liming Wang David E. Carlson Miguel R. D. Rodrigues David Wilcox A. Robert Calderbank Lawrence Carin
Memoized Online Variational Inference for Dirichlet Process Mixture Models.
A Kernel Test for Three-Variable Interactions.
Stochastic Convex Optimization with Multiple Objectives.
Lexical and Hierarchical Topic Regression.
Efficient Optimization for Sparse Gaussian Process Regression.
A Latent Source Model for Nonparametric Time Series Classification.
Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent.
Lasso Screening Rules via Dual Polytope Projection.
A Comparative Framework for Preconditioned Lasso Algorithms.
First-order Decomposition Trees.
Marginals-to-Models Reducibility.
A memory frontier for complex synapses.
High-Dimensional Gaussian Process Bandits.
Convex Relaxations for Permutation Problems.
Robust Spatial Filtering with Beta Divergence.
Wavelets on Graphs via Deep Learning.
When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements.
Linear Convergence with Condition Number Independent Access of Full Gradients.
Approximate inference in latent Gaussian-Markov models from continuous time observations.
Efficient Online Inference for Bayesian Nonparametric Relational Models.
Learning Adaptive Value of Information for Structured Prediction.
Estimating LASSO Risk and Noise Level.
Zero-Shot Learning Through Cross-Modal Transfer.
Reasoning With Neural Tensor Networks for Knowledge Base Completion.
Low-rank matrix reconstruction and clustering via approximate message passing.
Supervised Sparse Analysis and Synthesis Operators.
Generalized Denoising Auto-Encoders as Generative Models.
Speeding up Permutation Testing in Neuroimaging.
Regret based Robust Solutions for Uncertain Markov Decision Processes.
Direct 0-1 Loss Minimization and Margin Maximization with Boosting.
The Pareto Regret Frontier.
Distributed Exploration in Multi-Armed Bandits.
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms.
Low-Rank Matrix and Tensor Completion via Adaptive Sampling.
Robust Data-Driven Dynamic Programming.
Learning Multi-level Sparse Representations.
Learning a Deep Compact Image Representation for Visual Tracking.
Unsupervised Spectral Learning of Finite State Transducers.
Beyond Pairwise: Provably Fast Algorithms for Approximate k-Way Similarity Search.
Efficient Algorithm for Privately Releasing Smooth Queries.
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n).
Online PCA for Contaminated Data.
B-test: A Non-parametric, Low Variance Kernel Two-sample Test.
Learning Feature Selection Dependencies in Multi-task Learning.
A Gang of Bandits.
Latent Structured Active Learning.
Recurrent networks of coupled Winner-Take-All oscillators for solving constraint satisfaction problems.
On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization.
Reinforcement Learning in Robust Markov Decision Processes.
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation.
Conditional Random Fields via Univariate Exponential Families.
Mixed Optimization for Smooth Functions.
Projecting Ising Model Parameters for Fast Mixing.
Which Space Partitioning Tree to Use for Search?
Structured Learning via Logistic Regression.
Prior-free and prior-dependent regret bounds for Thompson Sampling.
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent.
Parallel Sampling of DP Mixture Models using Sub-Cluster Splits.
Dirty Statistical Models.
On Algorithms for Sparse Multi-factor NMF.
Neural representation of action sequences: how far can a simple snippet-matching model take us?
Large Scale Distributed Sparse Precision Estimation.
Learning Trajectory Preferences for Manipulators via Iterative Improvement.
Blind Calibration in Compressed Sensing using Message Passing Algorithms.
Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation.
Multi-Prediction Deep Boltzmann Machines.
Inferring neural population dynamics from multiple partial recordings of the same neural circuit.
Srinivas C. Turaga Lars Buesing Adam M. Packer Henry Dalgleish Noah Pettit Michael Häusser Jakob H. Macke
Learning Stochastic Feedforward Neural Networks.
A message-passing algorithm for multi-agent trajectory planning.
Auditing: Active Learning with Outcome-Dependent Query Costs.
q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions.
Mid-level Visual Element Discovery as Discriminative Mode Seeking.
Non-Linear Domain Adaptation with Boosting.
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima.
Rapid Distance-Based Outlier Detection via Sampling.
Better Approximation and Faster Algorithm Using the Proximal Average.
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture.
Correlated random features for fast semi-supervised learning.
Understanding variable importances in forests of randomized trees.
A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks.
Least Informative Dimensions.
Online Robust PCA via Stochastic Optimization.
Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation.
Improved and Generalized Upper Bounds on the Complexity of Policy Iteration.
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent.
Faster Ridge Regression via the Subsampled Randomized Hadamard Transform.
New Subsampling Algorithms for Fast Least Squares Regression.
Dropout Training as Adaptive Regularization.
On model selection consistency of penalized M-estimators: a geometric theory.
Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition.
Using multiple samples to learn mixture models.
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction.
Online Variational Approximations to non-Exponential Family Change Point Models: With Application to Radar Tracking.
Optimal Neural Population Codes for High-dimensional Stimulus Variables.
Correlations strike back (again): the case of associative memory retrieval.
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression.
One-shot learning and big data with n=2.
Summary Statistics for Partitionings and Feature Allocations.
Actor-Critic Algorithms for Risk-Sensitive MDPs.
What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach.
Decision Jungles: Compact and Rich Models for Classification.
Density estimation from unweighted k-nearest neighbor graphs: a roadmap.
Scalable kernels for graphs with continuous attributes.
Variational Policy Search via Trajectory Optimization.
A simple example of Dirichlet process mixture inconsistency for the number of components.
Training and Analysing Deep Recurrent Neural Networks.
Variance Reduction for Stochastic Gradient Optimization.
Sparse Additive Text Models with Low Rank Background.
Deep Fisher Networks for Large-Scale Image Classification.
Causal Inference on Time Series using Restricted Structural Equation Models.
More data speeds up training time in learning halfspaces over sparse vectors.
Transportability from Multiple Environments with Limited Experiments.
Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching.
Modeling Clutter Perception using Parametric Proto-object Partitioning.
PAC-Bayes-Empirical-Bernstein Inequality.
Point Based Value Iteration with Optimal Belief Compression for Dec-POMDPs.
On Decomposing the Proximal Map.
Polar Operators for Structured Sparse Estimation.
Generalized Random Utility Models with Multiple Types.
Provable Subspace Clustering: When LRR meets SSC.
Bayesian optimization explains human active search.
Transfer Learning in a Transductive Setting.
Data-driven Distributionally Robust Polynomial Optimization.
Latent Maximum Margin Clustering.
Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively.
Documents as multiple overlapping windows into grids of counts.
The Randomized Dependence Coefficient.