WhatToSee

Index of ICML 2007

• A Bound on the Lab el Complexity of Agnostic Active Learning
• A Dep endence Maximization View of Clustering
• A Fast Linear Separability Test by Pro jection of Positive Points on Subspaces
• A Kernel Path Algorithm for Supp ort Vector Machines
• A Kernel-based Causal Learning Algorithm
• A Novel Orthogonal NMF-Based Belief Compression for POMDPs
• A Permutation-Augmented Sampler for DP Mixture Mo dels
• A Recursive Metho d for Discriminative Mixture Learning
• A Transductive Framework of Distance Metric Learning by Sp ectral Dimensionality Reduction
• Adaptive Dimension Reduction Using Discriminant Analysis and K -means Clustering
• Adaptive Mesh Compression in 3D Computer Graphics using Multiscale Manifold Learning
• An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation
• An Integrated Approach to Feature Invention and Model Construction for Drug Activity Prediction
• Analyzing Feature Generation for Value-Function Approximation
• Approximate Maximum Margin Algorithms with Rules Controlled by the Numb er of Mistakes
• Asymmetric Boosting
• Asymptotic Bayesian Generalization Error when Training and Test Distributions are Different
• Automatic Shaping and Decomp osition of Reward Functions
• Bayesian Actor-Critic Algorithms
• Bayesian Compressive Sensing and Pro jection Optimization
• Beamforming using the Relevance Vector Machine
• Best of Both: A Hybridized Centroid-Medoid Clustering Heuristic
• Boosting for Transfer Learning
• Bottom-Up Learning of Markov Logic Network Structure
• Classifying Matrices with a Sp ectral Regularization
• Cluster Analysis of Heterogeneous Rank Data
• Combining Online and Offline Knowledge in UCT
• Comparisons of Sequence Labeling Algorithms and Extensions
• Conditional Random Fields for Multi-agent Reinforcement Learning
• Constructing Basis Functions from Directed Graphs for Value Function Approximation
• Cross-Domain Transfer for Reinforcement Learning
• Dimensionality Reduction and Generalization
• Direct Convex Relaxations of Sparse SVM
• Dirichlet Aggregation: Unsup ervised Learning towards an Optimal Metric for Prop ortional Data
• Discriminant Analysis in Correlation Similarity Measure Space
• Discriminant Kernel and Regularization Parameter Learning via Semidefinite Programming
• Discriminative Gaussian Process Latent Variable Model for Classification
• Discriminative Learning for Differing Training and Test Distributions
• Dynamic Hierarchical Markov Random Fields and their Application to Web Data Extraction
• Efficient Inference with Cardinality-based Clique Potentials
• Efficiently Computing Minimax Exp ected-Size Confidence Regions
• Entire Regularization Paths for Graph Data
• Experimental Persp ectives on Learning from Imbalanced Data
• Exponentiated Gradient Algorithms for Log-Linear Structured Prediction
• Fast and Effective Kernels for Relational Learning from Texts
• Feature Selection in a Kernel Space
• Focused Crawling with Scalable Ordinal Regression Solvers
• Full Regularization Path for Sparse Principal Comp onent Analysis
• Gradient Bo osting for Kernelized Output Spaces
• Graph Clustering with Network Structure Indices
• Hierarchical Gaussian Pro cess Latent Variable Mo dels
• Hierarchical Maximum Entropy Density Estimation
• Hybrid Hub erized Supp ort Vector Machines for Microarray Classification
• Incremental Bayesian Networks for Structure Prediction
• Infinite Mixtures of Trees
• Information-Theoretic Metric Learning
• Intractability and Clustering with Constraints
• Kernel Selection for Semi-Sup ervised Kernel Machines
• Kernelizing PLS, Degrees of Freedom, and Efficient Mo del Selection
• Large-scale RLSC Learning Without Agony
• Learning Distance Function by Co ding Similarity
• Learning Nonparametric Kernel Matrices from Pairwise Constraints
• Learning Random Walks to Rank No des in Graphs
• Learning State-Action Basis Functions for Hierarchical MDPs
• Learning a Meta-Level Prior for Feature Relevance from Multiple Related Tasks
• Learning for Efficient Retrieval of Structured Data with Noisy Queries
• Learning from Interpretations: A Ro oted Kernel for Ordered Hyp ergraphs
• Learning to Combine Distances for Complex Representations
• Learning to Compress Images and Videos
• Learning to Rank: From Pairwise Approach to Listwise Approach
• Least Squares Linear Discriminant Analysis
• Linear and Nonlinear Generative Probabilistic Class Mo dels for Shap e Contours
• Local Dep endent Comp onents
• Local Learning Pro jections
• Local Similarity Discriminant Analysis
• Magnitude-Preserving Ranking Algorithms
• Manifold-Adaptive Dimension Estimation
• Map Building without Lo calization by Dimensionality Reduction Techniques
• Maximum Margin Clustering Made Practical
• Minimum Reference Set Based Feature Selection for Small Sample Classifications
• Mixtures of Hierarchical Topics with Pachinko Allo cation
• Modeling Changing Dependency Structure in Multivariate Time Series
• More Efficiency in Multiple Kernel Learning
• Most Likely Heteroscedastic Gaussian Pro cess Regression
• Multi-Task Learning for Sequential Data via iHMMs and the Nested Dirichlet Pro cess
• Multi-Task Reinforcement Learning: A Hierarchical Bayesian Approach
• Multi-armed Bandit Problems with Dep endent Arms
• Multiclass Core Vector Machine
• Multiclass Multiple Kernel Learning
• Multifactor Gaussian Pro cess Mo dels for Style-Content Separation
• Multiple Instance Learning for Sparse Positive Bags
• Neighbor Search with Global Geometry: A Minimax Message Passing Algorithm
• Non-Isometric Manifold Learning: Analysis and an Algorithm
• Nonlinear Indep endent Comp onent Analysis with Minimal Nonlinear Distortion
• Nonmyopic Active Learning of Gaussian Pro cesses: An Exploration­Exploitation Approach
• On Learning Linear Ranking Functions for Beam Search
• On Learning with Dissimilarity Functions
• On One Metho d of Non-Diagonal Regularization in Sparse Bayesian Learning
• On the Relation Between Multi-Instance Learning and Semi-Sup ervised Learning
• On the Role of Tracking in Stationary Environments
• On the Value of Pairwise Constraints in Classification and Consistency
• Online Discovery of Similarity Mappings
• Online Kernel PCA with Entropic Matrix Up dates
• Optimal Dimensionality of Metric Space for Classification
• Parameter Learning for Relational Bayesian Networks
• Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
• Percentile Optimization in Uncertain Markov Decision Processes with Application to Efficient Exploration
• Piecewise Pseudolikeliho o d for Efficient Training of Conditional Random Fields
• Quadratically Gated Mixture of Exp erts for Incomplete Data Classification
• Quantum Clustering Algorithms
• Recovering Temp orally Rewiring Networks: A Mo del-based Approach
• Regression on Manifolds Using Kernel Dimension Reduction
• Reinforcement Learning by Reward-weighted Regression for Op erational Space Control
• Relational Clustering by Symmetric Convex Coding
• Restricted Boltzmann Machines for Collab orative Filtering
• Revisiting Probabilistic Models for Clustering with Pair-wise Constraints
• Robust Mixtures in the Presence of Measurement Errors
• Robust Multi-Task Learning with t-Pro cesses
• Robust Non-linear Dimensionality Reduction using Successive 1-Dimensional Laplacian Eigenmaps
• Sample Compression Bounds for Decision Trees
• Scalable Mo deling of Real Graphs using Kronecker Multiplication
• Scalable Training of L1 -Regularized Log-Linear Mo dels
• Self-taught Learning: Transfer Learning from Unlab eled Data
• Simple, Robust, Scalable Semi-sup ervised Learning via Exp ectation Regularization
• Simpler Core Vector Machines with Enclosing Balls
• Solving MultiClass Supp ort Vector Machines with LaRank
• Sparse Eigen Metho ds by D.C. Programming
• Sparse Probabilistic Classifiers
• Spectral Clustering and Transductive Learning with Multiple Views
• Spectral Feature Selection for Sup ervised and Unsup ervised Learning
• Statistical Predicate Invention
• Structural Alignment based Kernels for Protein Structure Classification
• Supervised Clustering of Streaming Data for Email Batch Detection
• Supervised Feature Selection via Dep endence Estimation
• Support Cluster Machine
• The Matrix Stick-Breaking Pro cess for Flexible Multi-Task Learning
• Three New Graphical Mo dels for Statistical Language Mo delling
• Tracking Value Function Dynamics to Improve Reinforcement Learning with Piecewise Linear Function Approximation
• Transductive Regression Piloted by Inter-Manifold Relations
• Transductive Supp ort Vector Machines for Structured Variables
• Trust Region Newton Metho ds for Large-Scale Logistic Regression
• Two-view Feature Generation Mo del for Semi-sup ervised Learning
• Uncovering Shared Structures in Multiclass Classification
• Unsupervised Estimation for Noisy-Channel Mo dels
• Unsupervised Prediction of Citation Influences
• What Is Decreased by the Max-sum Arc Consistency Algorithm?
• Winnowing Subspaces