A | B | C | D | E | F | G | H | J | K | L | M | N | O | P | R | S | T | V | W | X | Y | Z
Policy-Gradients for PSRs and POMDPs
Kernel Multi-task Learning using Task-specific Features
Generalized Non-metric Multidimensional Scaling
Information Retrieval by Inferring Implicit Queries from Eye Movements
A Boosting Algorithm for Label Covering in Multilabel Problems
(Approximate) Subgradient Methods for Structured Prediction
The Rademacher Complexity of Co-Regularized Kernel Classes
Generalized Non-metric Multidimensional Scaling
A Hybrid Pareto Model for Conditional Density Estimation of Asymmetric Fat-Tail Data
Mixture of Watson Distributions: A Generative Model for Hyperspherical Embeddings
A Bayesian Divergence Prior for Classiffier Adaptation
Kernel Multi-task Learning using Task-specific Features
A Unified Energy-Based Framework for Unsupervised Learning
Mixture of Watson Distributions: A Generative Model for Hyperspherical Embeddings
Policy-Gradients for PSRs and POMDPs
Efficient active learning with generalized linear models
A Hybrid Pareto Model for Conditional Density Estimation of Asymmetric Fat-Tail Data
The Laplacian Eigenmaps Latent Variable Model
Inductive Transfer for Bayesian Network Structure Learning
Generalized Non-metric Multidimensional Scaling
Learning Markov Structure by Maximum Entropy Relaxation
Deterministic Annealing for Multiple-Instance Learning
A Unified Energy-Based Framework for Unsupervised Learning
Visualizing Similarity Data with a Mixture of Maps
Solving Markov Random Fields with Spectral Relaxation
Fast search for Dirichlet process mixture models
SampleSearch: A Scheme that Searches for Consistent Samples
A Boosting Algorithm for Label Covering in Multilabel Problems
Large-Margin Classification in Banach Spaces
A Framework for Probability Density Estimation
Learning A* underestimates : Using inference to guide inference
Importance Sampling for General Hybrid Bayesian Networks
A fast algorithm for learning large scale preference relations
Exact Bayesian structure learning from uncertain interventions
Online Learning of Search Heuristics
Performance Guarantees for Information Theoretic Active Inference
Dynamic Factorization Tests: Applications to Multi-modal Data Association
A Stochastic Quasi-Newton Method for Online Convex Optimization
Deterministic Annealing for Multiple-Instance Learning
A Nonparametric Bayesian Approach to Modeling Overlapping Clusters
Analogical Reasoning with Relational Bayesian Sets
Local and global sparse Gaussian process approximations
Stick-breaking Construction for the Indian Buffet Process
Approximate inference using conditional entropy decompositions
Visualizing pairwise similarity via semidefinite programming
SampleSearch: A Scheme that Searches for Consistent Samples
Dissimilarity in Graph-Based Semi-Supervised Classification
A Latent Space Approach to Dynamic Embedding of Co-occurrence Data
Fast State Discovery for HMM Model Selection and Learning
A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games
Stick-breaking Construction for the Indian Buffet Process
Fast Mean Shift with Accurate and Stable Convergence
Recall Systems: Effcient Learning and Use of Category Indices
Fast Kernel ICA using an Approximate Newton Method
Mixture of Watson Distributions: A Generative Model for Hyperspherical Embeddings
Information Retrieval by Inferring Implicit Queries from Eye Movements
A Nonparametric Bayesian Approach to Modeling Overlapping Clusters
Analogical Reasoning with Relational Bayesian Sets
Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure
Visualizing Similarity Data with a Mixture of Maps
Learning Multilevel Distributed Representations for High-Dimensional Sequences
Multi-object tracking with representations of the symmetric group
Loopy Belief Propagation for Bipartite Maximum Weight b-Matching
Approximate inference using conditional entropy decompositions
Predictive Discretization during Model Selection
Loopy Belief Propagation for Bipartite Maximum Weight b-Matching
Multi-object tracking with representations of the symmetric group
Fast Kernel ICA using an Approximate Newton Method
Learning Markov Structure by Maximum Entropy Relaxation
Hierarchical Beta Processes and the Indian Buffet Process
Maximum Entropy Correlated Equilibria
Loop Corrected Belief Propagation
Information Retrieval by Inferring Implicit Queries from Eye Movements
Nonlinear Dimensionality Reduction as Information Retrieval
Recall Systems: Effcient Learning and Use of Category Indices
Multi-object tracking with representations of the symmetric group
MDL Histogram Density Estimation
Generalized Non-metric Multidimensional Scaling
A fast algorithm for learning large scale preference relations
Incorporating Prior Knowledge on Features into Learning
Fast Low-Rank Semidefinite Programming for Embedding and Clustering
Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo
Generalized Non-metric Multidimensional Scaling
Learning for Larger Datasets with the Gaussian Process Latent Variable Model
Learning Nearest-Neighbor Quantizers from Labeled Data by Information Loss Minimization
A Unified Energy-Based Framework for Unsupervised Learning
Treelets | A Tool for Dimensionality Reduction and Multi-Scale Analysis of Unstructured Data
Large-Margin Classification in Banach Spaces
Fast Mean Shift with Accurate and Stable Convergence
Efficient active learning with generalized linear models
A Bayesian Divergence Prior for Classiffier Adaptation
Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo
Fisher Consistency of Multicategory Support Vector Machines
The Kernel Path in Kernelized LASSO
The Laplacian Eigenmaps Latent Variable Model
Semi-supervised Clustering with Pairwise Constraints: A Discriminative Approach
Recall Systems: Effcient Learning and Use of Category Indices
AClass: A simple, online, parallelizable algorithm for probabilistic classification
A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games
Visualizing Similarity Data with a Mixture of Maps
Loop Corrected Belief Propagation
Margin based Transductive Graph Cuts using Linear Programming
Fast State Discovery for HMM Model Selection and Learning
Exact Bayesian structure learning from uncertain interventions
MDL Histogram Density Estimation
Treelets | A Tool for Dimensionality Reduction and Multi-Scale Analysis of Unstructured Data
Learning A* underestimates : Using inference to guide inference
Inductive Transfer for Bayesian Network Structure Learning
Approximate Counting of Graphical Models Via MCMC
Efficient active learning with generalized linear models
Approximate Counting of Graphical Models Via MCMC
Margin based Transductive Graph Cuts using Linear Programming
Fast Low-Rank Semidefinite Programming for Embedding and Clustering
Information Retrieval by Inferring Implicit Queries from Eye Movements
Learning Nearest-Neighbor Quantizers from Labeled Data by Information Loss Minimization
A Unified Energy-Based Framework for Unsupervised Learning
(Approximate) Subgradient Methods for Structured Prediction
A fast algorithm for learning large scale preference relations
Fast Mean Shift with Accurate and Stable Convergence
AClass: A simple, online, parallelizable algorithm for probabilistic classification
Loop Corrected Belief Propagation
The Rademacher Complexity of Co-Regularized Kernel Classes
Visualizing pairwise similarity via semidefinite programming
AClass: A simple, online, parallelizable algorithm for probabilistic classification
Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure
Recall Systems: Effcient Learning and Use of Category Indices
A Latent Space Approach to Dynamic Embedding of Co-occurrence Data
Approximate Counting of Graphical Models Via MCMC
Transductive Classification via Local Learning Regularization
A Stochastic Quasi-Newton Method for Online Convex Optimization
Bayesian Inference and Optimal Design in the Sparse Linear Model
A Unified Algorithmic Approach for Efficient Online Label Ranking
Margin based Transductive Graph Cuts using Linear Programming
A Framework for Probability Density Estimation
Information Retrieval by Inferring Implicit Queries from Eye Movements
Fast Kernel ICA using an Approximate Newton Method
Solving Markov Random Fields with Spectral Relaxation
A Latent Space Approach to Dynamic Embedding of Co-occurrence Data
Fast State Discovery for HMM Model Selection and Learning
Analogical Reasoning with Relational Bayesian Sets
A Boosting Algorithm for Label Covering in Multilabel Problems
A Unified Algorithmic Approach for Efficient Online Label Ranking
Dynamic Factorization Tests: Applications to Multi-modal Data Association
Generalized Darting Monte Carlo
Local and global sparse Gaussian process approximations
Predictive Discretization during Model Selection
Bayesian Inference and Optimal Design in the Sparse Linear Model
Emerge and spread models and word burstiness
Fast Low-Rank Semidefinite Programming for Embedding and Clustering
Visualizing Similarity Data with a Mixture of Maps
Learning Multilevel Distributed Representations for High-Dimensional Sequences
Margin based Transductive Graph Cuts using Linear Programming
Stick-breaking Construction for the Indian Buffet Process
AClass: A simple, online, parallelizable algorithm for probabilistic classification
Metric Learning for Kernel Regression
Hierarchical Beta Processes and the Indian Buffet Process
Policy-Gradients for PSRs and POMDPs
Incorporating Prior Knowledge on Features into Learnig
Bayesian Inference and Optimal Design in the Sparse Linear Model
Nonlinear Dimensionality Reduction as Information Retrieval
Learning A* underestimates : Using inference to guide inference
The Kernel Path in Kernelized LASSO
Efficient large margin semisupervised learning
Fast Mean Shift with Accurate and Stable Convergence
Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo
Metric Learning for Kernel Regression
Generalized Darting Monte Carlo
Loop Corrected Belief Propagation
Kernel Multi-task Learning using Task-specific Features
Performance Guarantees for Information Theoretic Active Inference
Generalized Non-metric Multidimensional Scaling
Learning Markov Structure by Maximum Entropy Relaxation
Performance Guarantees for Information Theoretic Active Inference
Dissimilarity in Graph-Based Semi-Supervised Classification
Transductive Classification via Local Learning Regularization
How Powerful Can Any Regression Learning Procedure Be?
The Kernel Path in Kernelized LASSO
A Stochastic Quasi-Newton Method for Online Convex Optimization
Importance Sampling for General Hybrid Bayesian Networks
Nonnegative Garrote Component Selection in Functional ANOVA models
Generalized Do-Calculus with Testable Causal Assumptions
Dissimilarity in Graph-Based Semi-Supervised Classification
(Approximate) Subgradient Methods for Structured Prediction
An Improved 1-norm SVM for Simultaneous Classification and Variable Selection