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