1532-4435

Journal of Machine Learning Research （JMLR） - Issue 19 论文列表

点击这里查看 Journal of Machine Learning Research 的JCR分区、影响因子等信息
卷期号： Issue 19
发布时间：
卷期年份： 2014
卷期官网：
本期论文列表
LIBOL: a library for online learning algorithms.

Recursive teaching dimension, VC-dimension and sample compression.

Node-based learning of multiple Gaussian graphical models.

Parallelizing exploration-exploitation tradeoffs in Gaussian process bandit optimization.

Do we need hundreds of classifiers to solve real world classification problems?

A junction tree framework for undirected graphical model selection.

Robust hierarchical clustering.

Clustering partially observed graphs via convex optimization.

Link prediction in graphs with autoregressive features.

On multilabel classification and ranking with bandit feedback.

Effective sampling and learning for mallows models with pairwise-preference data.

A reliable effective terascale linear learning system.

High-dimensional learning of linear causal networks via inverse covariance estimation.

Convex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ARD and GLasso.

Multimodal learning with deep Boltzmann machines.

Prediction and clustering in signed networks: a local to global perspective.

Efficient state-space inference of periodic latent force models.

Optimal data collection for informative rankings expose well-connected graphs.

Ground metric learning.

Efficient occlusive components analysis.

Graph estimation from multi-attribute data.

Expectation propagation for neural networks with sparsity-promoting priors.

Confidence intervals for random forests: the jackknife and the infinitesimal jackknife.

A novel M-estimator for robust PCA.

Accelerating t-SNE using tree-based algorithms.

Unbiased generative semi-supervised learning.

Active contextual policy search.

SPMF: a Java open-source pattern mining library.

Boosting algorithms for detector cascade learning.

Policy evaluation with temporal differences: a survey and comparison.

New learning methods for supervised and unsupervised preference aggregation.

Confidence intervals and hypothesis testing for high-dimensional regression.

Cover tree Bayesian reinforcement learning.

Pattern alternating maximization algorithm for missing data in high-dimensional problems.

Classifier cascades and trees for minimizing feature evaluation cost.

ooDACE toolbox: a flexible object-oriented Kriging implementation.

Fast SVM training using approximate extreme points.

Detecting click fraud in online advertising: a data mining approach.

Particle gibbs with ancestor sampling.

Improving Markov network structure learning using decision trees.

Surrogate regret bounds for bipartite ranking via strongly proper losses.

Revisiting Stein's paradox: multi-task averaging.

Fully simplified multivariate normal updates in non-conjugate variational message passing.

Sparse factor analysis for learning and content analytics.

Bayesian nonparametric comorbidity analysis of psychiatric disorders.

The fastclime package for linear programming and large-scale precision matrix estimation in R.

Efficient learning and planning with compressed predictive states.

Bayesian co-boosting for multi-modal gesture recognition.

QUIC: quadratic approximation for sparse inverse covariance estimation.

Axioms for graph clustering quality functions.

Improving prediction from dirichlet process mixtures via enrichment.

Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions.

Matrix completion with the trace norm: learning, bounding, and transducing.

Random intersection trees.

Ramp loss linear programming support vector machine.

A tensor approach to learning mixed membership community models.

The student-t mixture as a natural image patch prior with application to image compression.

The gesture recognition toolkit.

Statistical analysis of metric graph reconstruction.

Training highly multiclass classifiers.

Structured prediction via output space search.

Causal discovery with continuous additive noise models.

Order-independent constraint-based causal structure learning.

EnsembleSVM: a library for ensemble learning using support vector machines.

Bridging Viterbi and posterior decoding: a generalized risk approach to hidden path inference based on hidden Markov models.

One-shot-learning gesture recognition using HOG-HOF features.

Contextual bandits with similarity information.

Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization.

Effective string processing and matching for author disambiguation.

Locally adaptive factor processes for multivariate time series.

Off-policy learning with eligibility traces: a survey.

A truncated EM approach for spike-and-slab sparse coding.

An extension of slow feature analysis for nonlinear blind source separation.

Robust online gesture recognition with crowdsourced annotations.

Reinforcement learning for closed-loop propofol anesthesia: a study in human volunteers.

On the bayes-optimality of F-measure maximizers.

Natural evolution strategies.

Inconsistency of Pitman-Yor process mixtures for the number of components.

Alternating linearization for structured regularization problems.

Adaptive sampling for large scale boosting.

Iteration complexity of feasible descent methods for convex optimization.

Semi-supervised eigenvectors for large-scale locally-biased learning.

Follow the leader if you can, hedge if you must.

Transfer learning decision forests for gesture recognition.

Clustering hidden Markov models with variational HEM.

Spectral learning of latent-variable PCFGs: algorithms and sample complexity.

Convolutional nets and watershed cuts for real-time semantic Labeling of RGBD videos.

Bayesian entropy estimation for countable discrete distributions.

Optimality of graphlet screening in high dimensional variable selection.

What regularized auto-encoders learn from the data-generating distribution.

Asymptotic accuracy of distribution-based estimation of latent variables.

Learning graphical models with hubs.

Robust near-separable nonnegative matrix factorization using linear optimization.

Dropout: a simple way to prevent neural networks from overfitting.

High-dimensional covariance decomposition into sparse Markov and independence models.

Using trajectory data to improve bayesian optimization for reinforcement learning.

PyStruct: learning structured prediction in python.

Manopt, a matlab toolbox for optimization on manifolds.

Conditional random field with high-order dependencies for sequence labeling and segmentation.

Efficient and accurate methods for updating generalized linear models with multiple feature additions.

Ellipsoidal rounding for nonnegative matrix factorization under noisy separability.

BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits.

Revisiting Bayesian blind deconvolution.

Bayesian inference with posterior regularization and applications to infinite latent SVMs.

Information theoretical estimators toolbox.

Towards ultrahigh dimensional feature selection for big data.

Multi-objective reinforcement learning using sets of pareto dominating policies.

Parallel MCMC with generalized elliptical slice sampling.

Tensor decompositions for learning latent variable models.

Set-valued approachability and online learning with partial monitoring.

Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression.

Hitting and commute times in large random neighborhood graphs.

The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.

Active lmitation learning: formal and practical reductions to I.I.D. learning.

Seeded graph matching for correlated Erdös-Rényi graphs.

New results for random walk learning.

Active learning using smooth relative regret approximations with applications.

Adaptive minimax regression estimation over sparse lq-hulls.

Gibbs max-margin topic models with data augmentation.

Early stopping and non-parametric regression: an optimal data-dependent stopping rule.