icml 2009 论文列表Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009. |
Tutorial summary: Structured prediction for natural language processing.
Tutorial summary: Large social and information networks: opportunities for ML.
Tutorial summary: Active learning.
Tutorial summary: Machine learning in IR: recent successes and new opportunities.
Tutorial summary: The neuroscience of reinforcement learning.
Tutorial summary: Survey of boosting from an optimization perspective.
Tutorial summary: Learning with dependencies between several response variables.
Tutorial summary: Convergence of natural dynamics to equilibria.
Tutorial summary: Reductions in machine learning.
Workshop summary: Sparse methods for music audio.
Workshop summary: Abstraction in reinforcement learning.
Workshop summary: Numerical mathematics in machine learning.
Workshop summary: On-line learning with limited feedback.
Workshop summary: The fourth workshop on evaluation methods for machine learning.
Workshop summary: Results of the 2009 reinforcement learning competition.
Workshop summary: Workshop on learning feature hierarchies.
Workshop summary: Automated interpretation and modelling of cell images.
Workshop summary: Seventh annual workshop on Bayes applications.
Invited talk: Drifting games, boosting and online learning.
Invited talk: Can learning kernels help performance?
SimpleNPKL: simple non-parametric kernel learning.
On primal and dual sparsity of Markov networks.
MedLDA: maximum margin supervised topic models for regression and classification.
Multi-instance learning by treating instances as non-I.I.D. samples.
Learning non-redundant codebooks for classifying complex objects.
Prototype vector machine for large scale semi-supervised learning.
Learning instance specific distances using metric propagation.
Discovering options from example trajectories.
Compositional noisy-logical learning.
Interactively optimizing information retrieval systems as a dueling bandits problem.
Robust feature extraction via information theoretic learning.
Large-scale collaborative prediction using a nonparametric random effects model.
Piecewise-stationary bandit problems with side observations.
Learning structural SVMs with latent variables.
Stochastic search using the natural gradient.
Online learning by ellipsoid method.
Non-monotonic feature selection.
Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning.
A stochastic memoizer for sequence data.
Herding dynamical weights to learn.
Feature hashing for large scale multitask learning.
Evaluation methods for topic models.
K-means in space: a radiation sensitivity evaluation.
BoltzRank: learning to maximize expected ranking gain.
Model-free reinforcement learning as mixture learning.
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
More generality in efficient multiple kernel learning.
Ranking with ordered weighted pairwise classification.
Robot trajectory optimization using approximate inference.
Structure learning with independent non-identically distributed data.
Using fast weights to improve persistent contrastive divergence.
Factored conditional restricted Boltzmann Machines for modeling motion style.
Kernelized value function approximation for reinforcement learning.
Discriminative k-metrics.
Optimistic initialization and greediness lead to polynomial time learning in factored MDPs.
Fast gradient-descent methods for temporal-difference learning with linear function approximation.
Richard S. Sutton Hamid Reza Maei Doina Precup Shalabh Bhatnagar David Silver Csaba Szepesvári Eric Wiewiora
A simpler unified analysis of budget perceptrons.
A least squares formulation for a class of generalized eigenvalue problems in machine learning.
Multi-assignment clustering for Boolean data.
Hilbert space embeddings of conditional distributions with applications to dynamical systems.
Uncertainty sampling and transductive experimental design for active dual supervision.
Monte-Carlo simulation balancing.
Structure preserving embedding.
Stochastic methods for l1 regularized loss minimization.
Function factorization using warped Gaussian processes.
Ranking interesting subgroups.
Learning structurally consistent undirected probabilistic graphical models.
Surrogate regret bounds for proper losses.
Supervised learning from multiple experts: whom to trust when everyone lies a bit.
Vikas C. Raykar Shipeng Yu Linda H. Zhao Anna K. Jerebko Charles Florin Gerardo Hermosillo Valadez Luca Bogoni Linda Moy
The Bayesian group-Lasso for analyzing contingency tables.
Large-scale deep unsupervised learning using graphics processors.
Nearest neighbors in high-dimensional data: the emergence and influence of hubs.
An efficient projection for l1,infinity regularization.
Sparse higher order conditional random fields for improved sequence labeling.
An efficient sparse metric learning in high-dimensional space via l1-penalized log-determinant regularization.
Independent factor topic models.
Learning when to stop thinking and do something!
Multi-class image segmentation using conditional random fields and global classification.
Constraint relaxation in approximate linear programs.
Detecting the direction of causal time series.
Binary action search for learning continuous-action control policies.
Unsupervised hierarchical modeling of locomotion styles.
Nonparametric factor analysis with beta process priors.
Solution stability in linear programming relaxations: graph partitioning and unsupervised learning.
Convex variational Bayesian inference for large scale generalized linear models.
Learning complex motions by sequencing simpler motion templates.
Regression by dependence minimization and its application to causal inference in additive noise models.
Deep learning from temporal coherence in video.
Bandit-based optimization on graphs with application to library performance tuning.
Partial order embedding with multiple kernels.
Polyhedral outer approximations with application to natural language parsing.
Sparse Gaussian graphical models with unknown block structure.
Proto-predictive representation of states with simple recurrent temporal-difference networks.
Online dictionary learning for sparse coding.
Identifying suspicious URLs: an application of large-scale online learning.
Geometry-aware metric learning.
Topic-link LDA: joint models of topic and author community.
Efficient Euclidean projections in linear time.
Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery.
Learning from measurements in exponential families.
Semi-supervised learning using label mean.
ABC-boost: adaptive base class boost for multi-class classification.
Transfer learning for collaborative filtering via a rating-matrix generative model.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations.
Non-linear matrix factorization with Gaussian processes.
Learning nonlinear dynamic models.
Approximate inference for planning in stochastic relational worlds.
Generalization analysis of listwise learning-to-rank algorithms.
Block-wise construction of acyclic relational features with monotone irreducibility and relevancy properties.
Learning spectral graph transformations for link prediction.
On sampling-based approximate spectral decomposition.
Multiple indefinite kernel learning with mixed norm regularization.
Rule learning with monotonicity constraints.
The graphlet spectrum.
Regularization and feature selection in least-squares temporal difference learning.
Near-Bayesian exploration in polynomial time.
Learning Markov logic network structure via hypergraph lifting.
Boosting products of base classifiers.
Learning prediction suffix trees with Winnow.
A Bayesian approach to protein model quality assessment.
Orbit-product representation and correction of Gaussian belief propagation.
An accelerated gradient method for trace norm minimization.
Trajectory prediction: learning to map situations to robot trajectories.
Graph construction and b-matching for semi-supervised learning.
Group lasso with overlap and graph lasso.
Learning linear dynamical systems without sequence information.
Learning with structured sparsity.
Partially supervised feature selection with regularized linear models.
Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search.
Efficient learning algorithms for changing environments.
Bayesian clustering for email campaign detection.
Bayesian inference for Plackett-Luce ranking models.
Dynamic analysis of multiagent Q-learning with ε-greedy exploration.
Fast evolutionary maximum margin clustering.
PAC-Bayesian learning of linear classifiers.
Sequential Bayesian prediction in the presence of changepoints.
Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property.
Dynamic mixed membership blockmodel for evolving networks.
A majorization-minimization algorithm for (multiple) hyperparameter learning.
GAODE and HAODE: two proposals based on AODE to deal with continuous variables.
Learning to segment from a few well-selected training images.
Boosting with structural sparsity.
Domain adaptation from multiple sources via auxiliary classifiers.
Accounting for burstiness in topic models.
Accelerated sampling for the Indian Buffet Process.
Large margin training for hidden Markov models with partially observed states.
Proximal regularization for online and batch learning.
The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning.
A scalable framework for discovering coherent co-clusters in noisy data.
Good learners for evil teachers.
Analytic moment-based Gaussian process filtering.
Deep transfer via second-order Markov logic.
Unsupervised search-based structured prediction.
Fitting a graph to vector data.
EigenTransfer: a unified framework for transfer learning.
Nonparametric estimation of the precision-recall curve.
Exploiting sparse Markov and covariance structure in multiresolution models.
Learning dictionaries of stable autoregressive models for audio scene analysis.
Decision tree and instance-based learning for label ranking.
Matrix updates for perceptron training of continuous density hidden Markov models.
Learning kernels from indefinite similarities.
A convex formulation for learning shared structures from multiple tasks.
Multi-view clustering via canonical correlation analysis.
Robust bounds for classification via selective sampling.
Structure learning of Bayesian networks using constraints.
Probabilistic dyadic data analysis with local and global consistency.
Optimized expected information gain for nonlinear dynamical systems.
Active learning for directed exploration of complex systems.
Spectral clustering based on the graph p-Laplacian.
Online feature elicitation in interactive optimization.
Predictive representations for policy gradient in POMDPs.
Split variational inference.
Importance weighted active learning.
Curriculum learning.
Grammatical inference as a principal component analysis problem.
Incorporating domain knowledge into topic modeling via Dirichlet Forest priors.
Route kernels for trees.
Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities.
Archipelago: nonparametric Bayesian semi-supervised learning.