icml 2012 论文列表Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012. |
Agnostic System Identification for Model-Based Reinforcement Learning.
Nonparametric Link Prediction in Dynamic Networks.
Approximate Modified Policy Iteration.
Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription.
An Online Boosting Algorithm with Theoretical Justifications.
Linear Regression with Limited Observation.
Scene parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers.
Machine Learning that Matters.
Projection-free Online Learning.
Complexity Analysis of the Lasso Regularization Path.
Robust PCA in High-dimension: A Deterministic Approach.
Local Loss Optimization in Operator Models: A New Insight into Spectral Learning.
A Generative Process for Contractive Auto-Encoders.
Conditional mean embeddings as regressors.
Predicting Consumer Behavior in Commerce Search.
The Big Data Bootstrap.
An adaptive algorithm for finite stochastic partial monitoring.
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring.
Large Scale Variational Bayesian Inference for Structured Scale Mixture Models.
Statistical linear estimation with penalized estimators: an application to reinforcement learning.
Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret.
Discovering Support and Affiliated Features from Very High Dimensions.
High Dimensional Semiparametric Gaussian Copula Graphical Models.
Learning Task Grouping and Overlap in Multi-task Learning.
Convergence Rates for Differentially Private Statistical Estimation.
On the Sample Complexity of Reinforcement Learning with a Generative Model .
Online Alternating Direction Method.
Bayesian Conditional Cointegration.
Inferring Latent Structure From Mixed Real and Categorical Relational Data.
Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis.
On the Partition Function and Random Maximum A-Posteriori Perturbations.
Efficient Structured Prediction with Latent Variables for General Graphical Models.
Policy Gradients with Variance Related Risk Criteria.
Group Sparse Additive Models.
Copula-based Kernel Dependency Measures.
Marginalized Denoising Autoencoders for Domain Adaptation.
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation.
Approximate Principal Direction Trees.
Modelling transition dynamics in MDPs with RKHS embeddings.
Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds.
Submodular Inference of Diffusion Networks from Multiple Trees.
Clustering using Max-norm Constrained Optimization.
Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization.
Efficient Euclidean Projections onto the Intersection of Norm Balls.
Optimizing F-measure: A Tale of Two Approaches.
Path Integral Policy Improvement with Covariance Matrix Adaptation.
Efficient Decomposed Learning for Structured Prediction.
A Convex Feature Learning Formulation for Latent Task Structure Discovery.
Stochastic Smoothing for Nonsmooth Minimizations: Accelerating SGD by Exploiting Structure.
Sparse stochastic inference for latent Dirichlet allocation.
Communications Inspired Linear Discriminant Analysis.
Variational Inference in Non-negative Factorial Hidden Markov Models for Efficient Audio Source Separation.
Similarity Learning for Provably Accurate Sparse Linear Classification.
Utilizing Static Analysis and Code Generation to Accelerate Neural Networks.
Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations.
Poisoning Attacks against Support Vector Machines.
Improved Estimation in Time Varying Models.
Safe Exploration in Markov Decision Processes .
Learning Parameterized Skills.
A Joint Model of Language and Perception for Grounded Attribute Learning.
Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced Mixing Coefficients.
Deep Lambertian Networks.
The Nonparametric Metadata Dependent Relational Model.
Distributed Parameter Estimation via Pseudo-likelihood .
Anytime Marginal MAP Inference.
Large-Scale Feature Learning With Spike-and-Slab Sparse Coding.
Joint Optimization and Variable Selection of High-dimensional Gaussian Processes.
Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering.
Variational Bayesian Inference with Stochastic Search.
On the Equivalence between Herding and Conditional Gradient Algorithms.
Consistent Multilabel Ranking through Univariate Losses.
Learning Invariant Representations with Local Transformations.
A Binary Classification Framework for Two-Stage Multiple Kernel Learning.
Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events.
A convex relaxation for weakly supervised classifiers.
Canonical Trends: Detecting Trend Setters in Web Data.
The Greedy Miser: Learning under Test-time Budgets.
An Efficient Approach to Sparse Linear Discriminant Analysis.
Flexible Modeling of Latent Task Structures in Multitask Learning.
Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation.
Compositional Planning Using Optimal Option Models.
A Split-Merge Framework for Comparing Clusterings.
Shortest path distance in random k-nearest neighbor graphs.
Semi-Supervised Collective Classification via Hybrid Label Regularization.
Comparison-Based Learning with Rank Nets.
A Proximal-Gradient Homotopy Method for the L1-Regularized Least-Squares Problem.
Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching.
PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification.
Factorized Asymptotic Bayesian Hidden Markov Models.
Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Serie Modeling.
Discriminative Probabilistic Prototype Learning.
Max-Margin Nonparametric Latent Feature Models for Link Prediction.
Bayesian Watermark Attacks.
Training Restricted Boltzmann Machines on Word Observations.
Tighter Variational Representations of f-Divergences via Restriction to Probability Measures.
Analysis of Kernel Mean Matching under Covariate Shift.
Gaussian Process Regression Networks.
Revisiting k-means: New Algorithms via Bayesian Nonparametrics.
Deep Mixtures of Factor Analysers.
Learning to Identify Regular Expressions that Describe Email Campaigns.
Decoupling Exploration and Exploitation in Multi-Armed Bandits.
Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models.
A Complete Analysis of the l_1, p Group-Lasso.
A Hybrid Algorithm for Convex Semidefinite Optimization.
Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online Learning.
On multi-view feature learning.
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting.
Fast approximation of matrix coherence and statistical leverage.
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization.
No-Regret Learning in Extensive-Form Games with Imperfect Recall.
Quasi-Newton Methods: A New Direction.
Isoelastic Agents and Wealth Updates in Machine Learning Markets.
Agglomerative Bregman Clustering.
Robust Classification with Adiabatic Quantum Optimization.
Fast Prediction of New Feature Utility.
Fast Training of Nonlinear Embedding Algorithms.
Hierarchical Exploration for Accelerating Contextual Bandits.
Feature Selection via Probabilistic Outputs.
Estimating the Hessian by Back-propagating Curvature.
Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations.
Latent Multi-group Membership Graph Model.
Gaussian Process Quantile Regression using Expectation Propagation.
An Iterative Locally Linear Embedding Algorithm.
Plug-in martingales for testing exchangeability on-line.
Cross Language Text Classification via Subspace Co-regularized Multi-view Learning .
Learning Object Arrangements in 3D Scenes using Human Context.
Modeling Images using Transformed Indian Buffet Processes.
Apprenticeship Learning for Model Parameters of Partially Observable Environments.
Conditional Sparse Coding and Grouped Multivariate Regression.
Variance Function Estimation in High-dimensions.
Greedy Algorithms for Sparse Reinforcement Learning.
Lognormal and Gamma Mixed Negative Binomial Regression.
Incorporating Domain Knowledge in Matching Problems via Harmonic Analysis.
Bayesian Optimal Active Search and Surveying.
A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound.
Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization.
How To Grade a Test Without Knowing the Answers - A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing.
Output Space Search for Structured Prediction.
A Topic Model for Melodic Sequences.
Monte Carlo Bayesian Reinforcement Learning.
Hypothesis testing using pairwise distances and associated kernels.
AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem.
Exact Maximum Margin Structure Learning of Bayesian Networks.
Rethinking Collapsed Variational Bayes Inference for LDA.
A Combinatorial Algebraic Approach for the Identifiability of Low-Rank Matrix Completion.
Fast classification using sparse decision DAGs.
The Kernelized Stochastic Batch Perceptron.
Sparse Additive Functional and Kernel CCA.
State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction.
Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling.
Clustering by Low-Rank Doubly Stochastic Matrix Decomposition.
LPQP for MAP: Putting LP Solvers to Better Use.
Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems.
Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning .
Dirichlet Process with Mixed Random Measures: A Nonparametric Topic Model for Labeled Data.
Learning with Augmented Features for Heterogeneous Domain Adaptation.
Predicting accurate probabilities with a ranking loss.
A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices.
Is margin preserved after random projection?.
Consistent Covariance Selection From Data With Missing Values.
A Generalized Loop Correction Method for Approximate Inference in Graphical Models.
Learning the Dependence Graph of Time Series with Latent Factors.
Total Variation and Euler's Elastica for Supervised Learning.
Fast Computation of Subpath Kernel for Trees.
Multi-level Lasso for Sparse Multi-task Regression.
On the Size of the Online Kernel Sparsification Dictionary.
Influence Maximization in Continuous Time Diffusion Networks.
Improved Information Gain Estimates for Decision Tree Induction.
On-Line Portfolio Selection with Moving Average Reversion.
A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training.
Clustering to Maximize the Ratio of Split to Diameter.
Residual Components Analysis.
Manifold Relevance Determination.
A Unified Robust Classification Model.
Near-Optimal BRL using Optimistic Local Transitions.
Building high-level features using large scale unsupervised learning.
Quoc V. Le Marc'Aurelio Ranzato Rajat Monga Matthieu Devin Greg Corrado Kai Chen Jeffrey Dean Andrew Y. Ng
Levy Measure Decompositions for the Beta and Gamma Processes.
A Hierarchical Dirichlet Process Model with Multiple Levels of Clustering for Human EEG Seizure Modeling.
Convex Multitask Learning with Flexible Task Clusters.
Continuous Inverse Optimal Control with Locally Optimal Examples.
Compact Hyperplane Hashing with Bilinear Functions.
On causal and anticausal learning.
Lightning Does Not Strike Twice: Robust MDPs with Coupled Uncertainty.
Latent Collaborative Retrieval.
High-Dimensional Covariance Decomposition into Sparse Markov and Independence Domains.
Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs.
A fast and simple algorithm for training neural probabilistic language models.
Smoothness and Structure Learning by Proxy .
On Local Regret.
An Infinite Latent Attribute Model for Network Data.
Sequential Nonparametric Regression.
Maximum Margin Output Coding.
Structured Learning from Partial Annotations.
Cross-Domain Multitask Learning with Latent Probit Models.
Scaling Up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems.
A Dantzig Selector Approach to Temporal Difference Learning.
Sparse Support Vector Infinite Push.
Gap Filling in the Plant Kingdom - Trait Prediction Using Hierarchical Probabilistic Matrix Factorization.
Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization.
Hybrid Batch Bayesian Optimization.
Batch Active Learning via Coordinated Matching.
Adaptive Canonical Correlation Analysis Based On Matrix Manifolds.
Subgraph Matching Kernels for Attributed Graphs.
The Landmark Selection Method for Multiple Output Prediction.
Copula Mixture Model for Dependency-seeking Clustering.
Efficient Active Algorithms for Hierarchical Clustering.
Learning the Experts for Online Sequence Prediction.
Finding Botnets Using Minimal Graph Clusterings.
The Convexity and Design of Composite Multiclass Losses.
Nonparametric variational inference.
PAC Subset Selection in Stochastic Multi-armed Bandits.
Learning Efficient Structured Sparse Models.
The Most Persistent Soft-Clique in a Set of Sampled Graphs.
Learning to Label Aerial Images from Noisy Data.
Dimensionality Reduction by Local Discriminative Gaussians.
Modeling Latent Variable Uncertainty for Loss-based Learning.
Linear Off-Policy Actor-Critic.
Adaptive Regularization for Similarity Measures.
Stability of matrix factorization for collaborative filtering.
Groupwise Constrained Reconstruction for Subspace Clustering.
Ensemble Methods for Convex Regression with Applications to Geometric Programming Based Circuit Design.
Active Learning for Matching Problems.
Improved Nystrom Low-rank Decomposition with Priors.
Multiple Kernel Learning from Noisy Labels by Stochastic Programming.
Distributed Tree Kernels.
Exact Soft Confidence-Weighted Learning.
Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes.
Bayesian Efficient Multiple Kernel Learning.
Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss.
Bounded Planning in Passive POMDPs.
Using CCA to improve CCA: A new spectral method for estimating vector models of words.
Online Structured Prediction via Coactive Learning.
Estimation of Simultaneously Sparse and Low Rank Matrices.
Learning Force Control Policies for Compliant Robotic Manipulation.
On the Difficulty of Nearest Neighbor Search.
Two Manifold Problems with Applications to Nonlinear System Identification.
Robust Multiple Manifold Structure Learning.
TrueLabel + Confusions: A Spectrum of Probabilistic Models in Analyzing Multiple Ratings.
Capturing topical content with frequency and exclusivity.
Exemplar-SVMs for Visual Ob ject Detection, Label Transfer and Image Retrieval.
Learning the Central Events and Participants in Unlabeled Text.
Data-driven Web Design.
Conversational Speech Transcription Using Context-Dependent Deep Neural Networks.