icml 2014 论文列表Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014. |
Influence Function Learning in Information Diffusion Networks.
Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing.
A reversible infinite HMM using normalised random measures.
GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare.
Efficient Learning of Mahalanobis Metrics for Ranking.
Doubly Stochastic Variational Bayes for non-Conjugate Inference.
Deterministic Anytime Inference for Stochastic Continuous-Time Markov Processes.
Efficient Dimensionality Reduction for High-Dimensional Network Estimation.
Adaptive Monte Carlo via Bandit Allocation.
A Compilation Target for Probabilistic Programming Languages.
Sample-based approximate regularization.
Exponential Family Matrix Completion under Structural Constraints.
Learning Polynomials with Neural Networks.
Compositional Morphology for Word Representations and Language Modelling.
Finding Dense Subgraphs via Low-Rank Bilinear Optimization.
Universal Matrix Completion.
Estimating Latent-Variable Graphical Models using Moments and Likelihoods.
A Clockwork RNN.
Stochastic Variational Inference for Bayesian Time Series Models.
Learning from Contagion (Without Timestamps).
Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization.
Saddle Points and Accelerated Perceptron Algorithms.
Learning Character-level Representations for Part-of-Speech Tagging.
Beta Diffusion Trees.
Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors.
Neural Variational Inference and Learning in Belief Networks.
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets.
Multi-period Trading Prediction Markets with Connections to Machine Learning.
Towards End-To-End Speech Recognition with Recurrent Neural Networks.
Learning the Irreducible Representations of Commutative Lie Groups.
Learning Ordered Representations with Nested Dropout.
Active Learning of Parameterized Skills.
Nonnegative Sparse PCA with Provable Guarantees.
Learning by Stretching Deep Networks.
Spectral Regularization for Max-Margin Sequence Tagging.
Asynchronous Distributed ADMM for Consensus Optimization.
A Deep Semi-NMF Model for Learning Hidden Representations.
Stochastic Gradient Hamiltonian Monte Carlo.
Input Warping for Bayesian Optimization of Non-Stationary Functions.
Kernel Adaptive Metropolis-Hastings.
Scalable and Robust Bayesian Inference via the Median Posterior.
Structured Recurrent Temporal Restricted Boltzmann Machines.
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits.
Learnability of the Superset Label Learning Problem.
Multiresolution Matrix Factorization.
On learning to localize objects with minimal supervision.
Gaussian Approximation of Collective Graphical Models.
Adaptivity and Optimism: An Improved Exponentiated Gradient Algorithm.
A Bayesian Framework for Online Classifier Ensemble.
GeNGA: A Generalization of Natural Gradient Ascent with Positive and Negative Convergence Results.
Weighted Graph Clustering with Non-Uniform Uncertainties.
Online Stochastic Optimization under Correlated Bandit Feedback.
Dynamic Programming Boosting for Discriminative Macro-Action Discovery.
Riemannian Pursuit for Big Matrix Recovery.
The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation.
Pursuit-Evasion Without Regret, with an Application to Trading.
Probabilistic Matrix Factorization with Non-random Missing Data.
Programming by Feedback.
Fast Multi-stage Submodular Maximization.
Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations.
Marginalized Denoising Auto-encoders for Nonlinear Representations.
Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians.
Skip Context Tree Switching.
Probabilistic Partial Canonical Correlation Analysis.
Learning Modular Structures from Network Data and Node Variables.
Learning to Disentangle Factors of Variation with Manifold Interaction.
A Kernel Independence Test for Random Processes.
Discovering Latent Network Structure in Point Process Data.
Variational Inference for Sequential Distance Dependent Chinese Restaurant Process.
Effective Bayesian Modeling of Groups of Related Count Time Series.
Methods of Moments for Learning Stochastic Languages: Unified Presentation and Empirical Comparison.
Transductive Learning with Multi-class Volume Approximation.
High Order Regularization for Semi-Supervised Learning of Structured Output Problems.
Randomized Nonlinear Component Analysis.
Time-Regularized Interrupting Options (TRIO).
Memory and Computation Efficient PCA via Very Sparse Random Projections.
Sample Efficient Reinforcement Learning with Gaussian Processes.
Stable and Efficient Representation Learning with Nonnegativity Constraints.
Approximate Policy Iteration Schemes: A Comparison.
Active Transfer Learning under Model Shift.
Robust and Efficient Kernel Hyperparameter Paths with Guarantees.
One Practical Algorithm for Both Stochastic and Adversarial Bandits.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models.
Learning Latent Variable Gaussian Graphical Models.
Inferning with High Girth Graphical Models.
A Convergence Rate Analysis for LogitBoost, MART and Their Variant.
Deep AutoRegressive Networks.
Discrete Chebyshev Classifiers.
Learning the Parameters of Determinantal Point Process Kernels.
Affinity Weighted Embedding.
Online Multi-Task Learning for Policy Gradient Methods.
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models.
Distributed Representations of Sentences and Documents.
Deep Boosting.
Dual Query: Practical Private Query Release for High Dimensional Data.
Nonlinear Information-Theoretic Compressive Measurement Design.
Preserving Modes and Messages via Diverse Particle Selection.
Standardized Mutual Information for Clustering Comparisons: One Step Further in Adjustment for Chance.
Ensemble Methods for Structured Prediction.
Finito: A faster, permutable incremental gradient method for big data problems.
Latent Confusion Analysis by Normalized Gamma Construction.
Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series Data.
Outlier Path: A Homotopy Algorithm for Robust SVM.
A Physics-Based Model Prior for Object-Oriented MDPs.
Hierarchical Conditional Random Fields for Outlier Detection: An Application to Detecting Epileptogenic Cortical Malformations.
Bilal Ahmed Thomas Thesen Karen E. Blackmon Yijun Zhao Orrin Devinsky Ruben Kuzniecky Carla E. Brodley
Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows.
Optimal Mean Robust Principal Component Analysis.
Nearest Neighbors Using Compact Sparse Codes.
Distributed Stochastic Gradient MCMC.
Min-Max Problems on Factor Graphs.
A Bayesian Wilcoxon signed-rank test based on the Dirichlet process.
Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flow.
Concept Drift Detection Through Resampling.
Communication-Efficient Distributed Optimization using an Approximate Newton-type Method.
A PAC-Bayesian bound for Lifelong Learning.
Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito Process.
Hierarchical Dirichlet Scaling Process.
Making Fisher Discriminant Analysis Scalable.
Multiple Testing under Dependence via Semiparametric Graphical Models.
Circulant Binary Embedding.
Bayesian Optimization with Inequality Constraints.
Robust Inverse Covariance Estimation under Noisy Measurements.
Nonparametric Estimation of Renyi Divergence and Friends.
Sparse meta-Gaussian information bottleneck.
Combinatorial Partial Monitoring Game with Linear Feedback and Its Applications.
Maximum Margin Multiclass Nearest Neighbors.
Hard-Margin Active Linear Regression.
Joint Inference of Multiple Label Types in Large Networks.
Large-margin Weakly Supervised Dimensionality Reduction.
Reducing Dueling Bandits to Cardinal Bandits.
Local Ordinal Embedding.
Composite Quantization for Approximate Nearest Neighbor Search.
Learning Complex Neural Network Policies with Trajectory Optimization.
Efficient Algorithms for Robust One-bit Compressive Sensing.
Putting MRFs on a Tensor Train.
Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising.
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm.
Efficient Label Propagation.
Multivariate Maximal Correlation Analysis.
Cold-start Active Learning with Robust Ordinal Matrix Factorization.
Online Clustering of Bandits.
A Unifying View of Representer Theorems.
Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes.
The Falling Factorial Basis and Its Statistical Applications.
Learning Mixtures of Linear Classifiers.
K-means recovers ICA filters when independent components are sparse.
Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data.
Global graph kernels using geometric embeddings.
Fast Computation of Wasserstein Barycenters.
Coding for Random Projections.
Statistical analysis of stochastic gradient methods for generalized linear models.
A Single-Pass Algorithm for Efficiently Recovering Sparse Cluster Centers of High-dimensional Data.
Structured Generative Models of Natural Source Code.
Nonparametric Estimation of Multi-View Latent Variable Models.
Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification.
Stochastic Neighbor Compression.
Alternating Minimization for Mixed Linear Regression.
Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods.
Multimodal Neural Language Models.
Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting.
On Robustness and Regularization of Structural Support Vector Machines.
A new Q(lambda) with interim forward view and Monte Carlo equivalence.
Model-Based Relational RL When Object Existence is Partially Observable.
Local algorithms for interactive clustering.
Least Squares Revisited: Scalable Approaches for Multi-class Prediction.
Latent Semantic Representation Learning for Scene Classification.
Scaling SVM and Least Absolute Deviations via Exact Data Reduction.
Memory (and Time) Efficient Sequential Monte Carlo.
Linear Time Solver for Primal SVM.
Linear Programming for Large-Scale Markov Decision Problems.
Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm.
Consistency of Causal Inference under the Additive Noise Model.
An Asynchronous Parallel Stochastic Coordinate Descent Algorithm.
Optimization Equivalence of Divergences Improves Neighbor Embedding.
An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy.
Structured Prediction of Network Response.
Gaussian Process Classification and Active Learning with Multiple Annotators.
Sparse Reinforcement Learning via Convex Optimization.
Bayesian Max-margin Multi-Task Learning with Data Augmentation.
Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically.
Elementary Estimators for Sparse Covariance Matrices and other Structured Moments.
Elementary Estimators for High-Dimensional Linear Regression.
Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices.
Two-Stage Metric Learning.
Rectangular Tiling Process.
Hierarchical Quasi-Clustering Methods for Asymmetric Networks.
Anomaly Ranking as Supervised Bipartite Ranking.
Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications.
Multi-label Classification via Feature-aware Implicit Label Space Encoding.
PAC-inspired Option Discovery in Lifelong Reinforcement Learning.
Signal recovery from Pooling Representations.
Learning the Consistent Behavior of Common Users for Target Node Prediction across Social Networks.
Safe Screening with Variational Inequalities and Its Application to Lasso.
Clustering in the Presence of Background Noise.
Exchangeable Variable Models.
Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy.
Gaussian Process Optimization with Mutual Information.
Statistical-Computational Phase Transitions in Planted Models: The High-Dimensional Setting.
A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models.
Deep Generative Stochastic Networks Trainable by Backprop.
Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing.
Pitfalls in the use of Parallel Inference for the Dirichlet Process.
Linear and Parallel Learning of Markov Random Fields.
Marginal Structured SVM with Hidden Variables.
Scaling Up Robust MDPs using Function Approximation.
Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques.
On the convergence of no-regret learning in selfish routing.
Near-Optimally Teaching the Crowd to Classify.
Geodesic Distance Function Learning via Heat Flow on Vector Fields.
A Unified Framework for Consistency of Regularized Loss Minimizers.
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization.
On p-norm Path Following in Multiple Kernel Learning for Non-linear Feature Selection.
Convex Total Least Squares.
Near-Optimal Joint Object Matching via Convex Relaxation.
Rank-One Matrix Pursuit for Matrix Completion.
Automated inference of point of view from user interactions in collective intelligence venues.
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery.
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation.
Robust Principal Component Analysis with Complex Noise.
Spectral Bandits for Smooth Graph Functions.
Heavy-tailed regression with a generalized median-of-means.
Concentration in unbounded metric spaces and algorithmic stability.
Compact Random Feature Maps.
Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem.
An Information Geometry of Statistical Manifold Learning.
An Efficient Approach for Assessing Hyperparameter Importance.
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction.
Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers.
Filtering with Abstract Particles.
Hamiltonian Monte Carlo Without Detailed Balance.
Learning Sum-Product Networks with Direct and Indirect Variable Interactions.
Memory Efficient Kernel Approximation.
True Online TD(lambda).
Admixture of Poisson MRFs: A Topic Model with Word Dependencies.
Coherent Matrix Completion.
Narrowing the Gap: Random Forests In Theory and In Practice.
Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers.
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition.
Efficient Continuous-Time Markov Chain Estimation.
Spherical Hamiltonian Monte Carlo for Constrained Target Distributions.
Towards an optimal stochastic alternating direction method of multipliers.
Agnostic Bayesian Learning of Ensembles.
Learning Graphs with a Few Hubs.
Large-scale Multi-label Learning with Missing Labels.
Provable Bounds for Learning Some Deep Representations.
Nuclear Norm Minimization via Active Subspace Selection.
A Divide-and-Conquer Solver for Kernel Support Vector Machines.
Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search.
Coordinate-descent for learning orthogonal matrices through Givens rotations.
Asymptotically consistent estimation of the number of change points in highly dependent time series.
Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection.
Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms.
Online Learning in Markov Decision Processes with Changing Cost Sequences.
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint.
Discriminative Features via Generalized Eigenvectors.
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels.
(Near) Dimension Independent Risk Bounds for Differentially Private Learning.
A Deep and Tractable Density Estimator.
On Modelling Non-linear Topical Dependencies.
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning.
Bias in Natural Actor-Critic Algorithms.
On Measure Concentration of Random Maximum A-Posteriori Perturbations.
Condensed Filter Tree for Cost-Sensitive Multi-Label Classification.
Diagnosis determination: decision trees optimizing simultaneously worst and expected testing cost.
Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach.
Modeling Correlated Arrival Events with Latent Semi-Markov Processes.
Deterministic Policy Gradient Algorithms.
Online Bayesian Passive-Aggressive Learning.
Tracking Adversarial Targets.
Computing Parametric Ranking Models via Rank-Breaking.
Optimal Budget Allocation: Theoretical Guarantee and Efficient Algorithm.
Boosting with Online Binary Learners for the Multiclass Bandit Problem.
Generalized Exponential Concentration Inequality for Renyi Divergence Estimation.
Efficient Approximation of Cross-Validation for Kernel Methods using Bouligand Influence Function.
Max-Margin Infinite Hidden Markov Models.
Wasserstein Propagation for Semi-Supervised Learning.
Large-Margin Metric Learning for Constrained Partitioning Problems.
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts.
Prediction with Limited Advice and Multiarmed Bandits with Paid Observations.
Low-density Parity Constraints for Hashing-Based Discrete Integration.
Learning Theory and Algorithms for revenue optimization in second price auctions with reserve.
Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models.
Margins, Kernels and Non-linear Smoothed Perceptrons.
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball.
Towards Minimax Online Learning with Unknown Time Horizon.
Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data.
A Consistent Histogram Estimator for Exchangeable Graph Models.
The Inverse Regression Topic Model.
Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis.
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget.
Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs.
Convergence rates for persistence diagram estimation in Topological Data Analysis.
Von Mises-Fisher Clustering Models.
Fast Allocation of Gaussian Process Experts.
Latent Bandits.
Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations.
A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise Data.
Boosting multi-step autoregressive forecasts.
Thompson Sampling for Complex Online Problems.
A Statistical Perspective on Algorithmic Leveraging.
Recurrent Convolutional Neural Networks for Scene Labeling.
An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization.
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization.
Active Detection via Adaptive Submodularity.
Fast Stochastic Alternating Direction Method of Multipliers.
The Coherent Loss Function for Classification.
Covering Number for Efficient Heuristic-based POMDP Planning.
Demystifying Information-Theoretic Clustering.
Kernel Mean Estimation and Stein Effect.
A Discriminative Latent Variable Model for Online Clustering.