icml 2013 论文列表Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013. |
Estimation of Causal Peer Influence Effects.
On A Nonlinear Generalization of Sparse Coding and Dictionary Learning.
Coco-Q: Learning in Stochastic Games with Side Payments.
Adaptive Hamiltonian and Riemann Manifold Monte Carlo.
Online Learning under Delayed Feedback.
Multilinear Multitask Learning.
Tree-Independent Dual-Tree Algorithms.
Ryan R. Curtin William B. March Parikshit Ram David V. Anderson Alexander G. Gray Charles L. Isbell Jr.
Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling.
Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration.
Learning the beta-Divergence in Tweedie Compound Poisson Matrix Factorization Models.
Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion.
Cost-sensitive Multiclass Classification Risk Bounds.
Safe Screening of Non-Support Vectors in Pathwise SVM Computation.
Manifold Preserving Hierarchical Topic Models for Quantization and Approximation.
Learning Policies for Contextual Submodular Prediction.
Scale Invariant Conditional Dependence Measures.
Nonparametric Mixture of Gaussian Processes with Constraints.
Deep learning with COTS HPC systems.
Predictable Dual-View Hashing.
Maxout Networks.
On the difficulty of training recurrent neural networks.
Learning Triggering Kernels for Multi-dimensional Hawkes Processes.
Multiple-source cross-validation.
Expensive Function Optimization with Stochastic Binary Outcomes.
Fast Image Tagging.
Sparse Gaussian Conditional Random Fields: Algorithms, Theory, and Application to Energy Forecasting.
Consistency of Online Random Forests.
Deep Canonical Correlation Analysis.
Almost Optimal Exploration in Multi-Armed Bandits.
The Bigraphical Lasso.
Selective sampling algorithms for cost-sensitive multiclass prediction.
Bayesian Learning of Recursively Factored Environments.
Topic Discovery through Data Dependent and Random Projections.
The Cross-Entropy Method Optimizes for Quantiles.
Smooth Operators.
Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events.
Structure Discovery in Nonparametric Regression through Compositional Kernel Search.
Intersecting singularities for multi-structured estimation.
A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines.
On the importance of initialization and momentum in deep learning.
Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization.
O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions.
Exploiting Ontology Structures and Unlabeled Data for Learning.
The Extended Parameter Filter.
Top-k Selection based on Adaptive Sampling of Noisy Preferences.
Algorithms for Direct 0-1 Loss Optimization in Binary Classification.
Anytime Representation Learning.
Gaussian Process Kernels for Pattern Discovery and Extrapolation.
Regularization of Neural Networks using DropConnect.
Distribution to Distribution Regression.
Spectral Experts for Estimating Mixtures of Linear Regressions.
The lasso, persistence, and cross-validation.
Mini-Batch Primal and Dual Methods for SVMs.
Scaling the Indian Buffet Process via Submodular Maximization.
Approximate Inference in Collective Graphical Models.
Breaking the Small Cluster Barrier of Graph Clustering.
Natural Image Bases to Represent Neuroimaging Data.
Fast Max-Margin Matrix Factorization with Data Augmentation.
Dependent Normalized Random Measures.
Modeling Temporal Evolution and Multiscale Structure in Networks.
Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models.
Stability and Hypothesis Transfer Learning.
Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner.
Concurrent Reinforcement Learning from Customer Interactions.
Learning Convex QP Relaxations for Structured Prediction.
One-Pass AUC Optimization.
Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines.
Robust Regression on MapReduce.
Quantile Regression for Large-scale Applications.
Learning the Structure of Sum-Product Networks.
Kernelized Bayesian Matrix Factorization.
Fast Semidifferential-based Submodular Function Optimization.
Max-Margin Multiple-Instance Dictionary Learning.
Stable Coactive Learning via Perturbation.
Collective Stability in Structured Prediction: Generalization from One Example.
Domain Adaptation under Target and Conditional Shift.
Feature Multi-Selection among Subjective Features.
Consistency versus Realizable H-Consistency for Multiclass Classification.
Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation.
Optimization with First-Order Surrogate Functions.
Robust Sparse Regression under Adversarial Corruption.
Exact Rule Learning via Boolean Compressed Sensing.
Computation-Risk Tradeoffs for Covariance-Thresholded Regression.
Sparse PCA through Low-rank Approximations.
A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers.
Infinite Markov-Switching Maximum Entropy Discrimination Machines.
On autoencoder scoring.
LDA Topic Model with Soft Assignment of Descriptors to Words.
On learning parametric-output HMMs.
Sharp Generalization Error Bounds for Randomly-projected Classifiers.
ABC Reinforcement Learning.
Better Rates for Any Adversarial Deterministic MDP.
Modeling Information Propagation with Survival Theory.
Factorial Multi-Task Learning : A Bayesian Nonparametric Approach.
Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training.
Analogy-preserving Semantic Embedding for Visual Object Categorization.
Spectral Learning of Hidden Markov Models from Dynamic and Static Data.
Online Kernel Learning with a Near Optimal Sparsity Bound.
Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment.
On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance.
Quickly Boosting Decision Trees - Pruning Underachieving Features Early.
A Unified Robust Regression Model for Lasso-like Algorithms.
Infinite Positive Semidefinite Tensor Factorization for Source Separation of Mixture Signals.
Revisiting the Nystrom method for improved large-scale machine learning.
Dynamic Covariance Models for Multivariate Financial Time Series.
On Compact Codes for Spatially Pooled Features.
Riemannian Similarity Learning.
Scalable Simple Random Sampling and Stratified Sampling.
Loss-Proportional Subsampling for Subsequent ERM.
Parameter Learning and Convergent Inference for Dense Random Fields.
\(\propto\)SVM for Learning with Label Proportions.
Temporal Difference Methods for the Variance of the Reward To Go.
Local Deep Kernel Learning for Efficient Non-linear SVM Prediction.
Entropic Affinities: Properties and Efficient Numerical Computation.
That was fast! Speeding up NN search of high dimensional distributions.
Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy.
Non-Linear Stationary Subspace Analysis with Application to Video Classification.
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions.
Simple Sparsification Improves Sparse Denoising Autoencoders in Denoising Highly Corrupted Images.
Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model.
Spectral Compressed Sensing via Structured Matrix Completion.
Efficient Multi-label Classification with Many Labels.
A Local Algorithm for Finding Well-Connected Clusters.
Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels.
Learning Heteroscedastic Models by Convex Programming under Group Sparsity.
Solving Continuous POMDPs: Value Iteration with Incremental Learning of an Efficient Space Representation.
Planning by Prioritized Sweeping with Small Backups.
Multi-View Clustering and Feature Learning via Structured Sparsity.
No more pesky learning rates.
Hierarchical Tensor Decomposition of Latent Tree Graphical Models.
Learning Fair Representations.
Unfolding Latent Tree Structures using 4th Order Tensors.
Safe Policy Iteration.
Robust and Discriminative Self-Taught Learning.
Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations.
Top-down particle filtering for Bayesian decision trees.
Mean Reversion with a Variance Threshold.
Message passing with l1 penalized KL minimization.
Joint Transfer and Batch-mode Active Learning.
Fastfood - Computing Hilbert Space Expansions in loglinear time.
The Most Generative Maximum Margin Bayesian Networks.
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes.
Estimating Unknown Sparsity in Compressed Sensing.
An Efficient Posterior Regularized Latent Variable Model for Interactive Sound Source Separation.
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning.
A Fast and Exact Energy Minimization Algorithm for Cycle MRFs.
Learning from Human-Generated Lists.
Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression.
Tensor Analyzers.
One-Bit Compressed Sensing: Provable Support and Vector Recovery.
Inference algorithms for pattern-based CRFs on sequence data.
Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller Space.
Thompson Sampling for Contextual Bandits with Linear Payoffs.
Differentially Private Learning with Kernels.
Efficient Ranking from Pairwise Comparisons.
Learning invariant features by harnessing the aperture problem.
Guaranteed Sparse Recovery under Linear Transformation.
MILEAGE: Multiple Instance LEArning with Global Embedding.
Markov Network Estimation From Multi-attribute Data.
Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing.
Bayesian Games for Adversarial Regression Problems.
Multi-Class Classification with Maximum Margin Multiple Kernel.
Hierarchical Regularization Cascade for Joint Learning.
The Sample-Complexity of General Reinforcement Learning.
Gossip-based distributed stochastic bandit algorithms.
Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning.
Guided Policy Search.
Activized Learning with Uniform Classification Noise.
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data.
Direct Modeling of Complex Invariances for Visual Object Features.
Sparse coding for multitask and transfer learning.
Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization.
Large-Scale Learning with Less RAM via Randomization.
Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment.
Margins, Shrinkage, and Boosting.
An Adaptive Learning Rate for Stochastic Variational Inference.
Distributed training of Large-scale Logistic models.
A Practical Algorithm for Topic Modeling with Provable Guarantees.
Sanjeev Arora Rong Ge Yonatan Halpern David M. Mimno Ankur Moitra David A. Sontag Yichen Wu Michael Zhu
A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions.
Exploring the Mind: Integrating Questionnaires and fMRI.
Subtle Topic Models and Discovering Subtly Manifested Software Concerns Automatically.
Modeling Musical Influence with Topic Models.
Sparse projections onto the simplex.
Sequential Bayesian Search.
Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines.
SADA: A General Framework to Support Robust Causation Discovery.
Collaborative hyperparameter tuning.
Subproblem-Tree Calibration: A Unified Approach to Max-Product Message Passing.
Label Partitioning For Sublinear Ranking.
Transition Matrix Estimation in High Dimensional Time Series.
Strict Monotonicity of Sum of Squares Error and Normalized Cut in the Lattice of Clusterings.
Gated Autoencoders with Tied Input Weights.
Monochromatic Bi-Clustering.
Precision-recall space to correct external indices for biclustering.
Scalable Optimization of Neighbor Embedding for Visualization.
Fast dropout training.
Learning Connections in Financial Time Series.
A unifying framework for vector-valued manifold regularization and multi-view learning.
Generic Exploration and K-armed Voting Bandits.
Local Low-Rank Matrix Approximation.
Ellipsoidal Multiple Instance Learning.
Forecastable Component Analysis.
A Variational Approximation for Topic Modeling of Hierarchical Corpora.
Thurstonian Boltzmann Machines: Learning from Multiple Inequalities.
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems.
Toward Optimal Stratification for Stratified Monte-Carlo Integration.
Stochastic Simultaneous Optimistic Optimization.
Gaussian Process Vine Copulas for Multivariate Dependence.
Mixture of Mutually Exciting Processes for Viral Diffusion.
Rounding Methods for Discrete Linear Classification.
Convex Adversarial Collective Classification.
Efficient Semi-supervised and Active Learning of Disjunctions.
Constrained fractional set programs and their application in local clustering and community detection.
Robust Structural Metric Learning.
Learning an Internal Dynamics Model from Control Demonstration.
Vanishing Component Analysis.
Large-Scale Bandit Problems and KWIK Learning.
Dynamical Models and tracking regret in online convex programming.
Characterizing the Representer Theorem.
Online Latent Dirichlet Allocation with Infinite Vocabulary.
Better Mixing via Deep Representations.
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning.
Adaptive Task Assignment for Crowdsourced Classification.
Convex Relaxations for Learning Bounded-Treewidth Decomposable Graphs.
A Structural SVM Based Approach for Optimizing Partial AUC.
ELLA: An Efficient Lifelong Learning Algorithm.
Online Feature Selection for Model-based Reinforcement Learning.
Enhanced statistical rankings via targeted data collection.
Efficient Active Learning of Halfspaces: an Aggressive Approach.
A Generalized Kernel Approach to Structured Output Learning.
Active Learning for Multi-Objective Optimization.
Scaling Multidimensional Gaussian Processes using Projected Additive Approximations.
Iterative Learning and Denoising in Convolutional Neural Associative Memories.
General Functional Matrix Factorization Using Gradient Boosting.
Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization.
Approximation properties of DBNs with binary hidden units and real-valued visible units.
Learning with Marginalized Corrupted Features.
A New Frontier of Kernel Design for Structured Data.
Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method.
Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery.
A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning.
Optimal rates for stochastic convex optimization under Tsybakov noise condition.
Parsing epileptic events using a Markov switching process model for correlated time series.
Efficient Dimensionality Reduction for Canonical Correlation Analysis.
Human Boosting.
Feature Selection in High-Dimensional Classification.
Average Reward Optimization Objective In Partially Observable Domains.
Adaptive Sparsity in Gaussian Graphical Models.
Maximum Variance Correction with Application to A* Search.
Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model.
Efficient Sparse Group Feature Selection via Nonconvex Optimization.
Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks.
Learning Optimally Sparse Support Vector Machines.
Multiple Identifications in Multi-Armed Bandits.
Learning Linear Bayesian Networks with Latent Variables.
Principal Component Analysis on non-Gaussian Dependent Data.
Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization.
Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation.
Fixed-Point Model For Structured Labeling.
The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification.
Discriminatively Activated Sparselets.
A Machine Learning Framework for Programming by Example.
Modelling Sparse Dynamical Systems with Compressed Predictive State Representations.
Convex formulations of radius-margin based Support Vector Machines.
Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization.
Combinatorial Multi-Armed Bandit: General Framework and Applications.
Learning Hash Functions Using Column Generation.
Cost-Sensitive Tree of Classifiers.
Gibbs Max-Margin Topic Models with Fast Sampling Algorithms.
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures.
Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction.
Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models.
Noisy Sparse Subspace Clustering.
Stochastic Alternating Direction Method of Multipliers.
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes.
Fast Probabilistic Optimization from Noisy Gradients.
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs.
Sparse Uncorrelated Linear Discriminant Analysis.
Sparsity-Based Generalization Bounds for Predictive Sparse Coding.
Near-Optimal Bounds for Cross-Validation via Loss Stability.
A Spectral Learning Approach to Range-Only SLAM.
Domain Generalization via Invariant Feature Representation.
An Optimal Policy for Target Localization with Application to Electron Microscopy.